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You searched for subject:(YOLO). Showing records 1 – 30 of 45 total matches.

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California State University – Sacramento

1. Rico, Leah Marisa. The Early Academic Outreach Program at UC Davis and social factors present for Latino/a participants.

Degree: MSW, Social Work, 2011, California State University – Sacramento

 Leah Marisa Rico and Gloria Teresa Rodriguez-Rooks collaborated equally in all phases of this research project which included identification of the research problem, review of… (more)

Subjects/Keywords: Postsecondary education; Yolo County; TRiO

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APA (6th Edition):

Rico, L. M. (2011). The Early Academic Outreach Program at UC Davis and social factors present for Latino/a participants. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.9/1207

Chicago Manual of Style (16th Edition):

Rico, Leah Marisa. “The Early Academic Outreach Program at UC Davis and social factors present for Latino/a participants.” 2011. Masters Thesis, California State University – Sacramento. Accessed January 22, 2021. http://hdl.handle.net/10211.9/1207.

MLA Handbook (7th Edition):

Rico, Leah Marisa. “The Early Academic Outreach Program at UC Davis and social factors present for Latino/a participants.” 2011. Web. 22 Jan 2021.

Vancouver:

Rico LM. The Early Academic Outreach Program at UC Davis and social factors present for Latino/a participants. [Internet] [Masters thesis]. California State University – Sacramento; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10211.9/1207.

Council of Science Editors:

Rico LM. The Early Academic Outreach Program at UC Davis and social factors present for Latino/a participants. [Masters Thesis]. California State University – Sacramento; 2011. Available from: http://hdl.handle.net/10211.9/1207


KTH

2. Sun, Ruiwen. Detecting Faulty Tape-around Weatherproofing Cables by Computer Vision.

Degree: Electrical Engineering and Computer Science (EECS), 2020, KTH

More cables will be installed owing to setting up more radio towers when it comes to 5G. However, a large proportion of radio units… (more)

Subjects/Keywords: Object Detection; Image Processing; OpenCV; YOLO; Line Segment Detector; Objektdetektion; Bildbehandling; OpenCV; YOLO; Linjesegmentdetektor; Computer and Information Sciences; Data- och informationsvetenskap

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APA (6th Edition):

Sun, R. (2020). Detecting Faulty Tape-around Weatherproofing Cables by Computer Vision. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272108

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sun, Ruiwen. “Detecting Faulty Tape-around Weatherproofing Cables by Computer Vision.” 2020. Thesis, KTH. Accessed January 22, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272108.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sun, Ruiwen. “Detecting Faulty Tape-around Weatherproofing Cables by Computer Vision.” 2020. Web. 22 Jan 2021.

Vancouver:

Sun R. Detecting Faulty Tape-around Weatherproofing Cables by Computer Vision. [Internet] [Thesis]. KTH; 2020. [cited 2021 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272108.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sun R. Detecting Faulty Tape-around Weatherproofing Cables by Computer Vision. [Thesis]. KTH; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272108

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

3. Bhat, Aneesh. Aerial Object Detection using Learnable Bounding Boxes.

Degree: MS, Computer Engineering, 2019, Rochester Institute of Technology

  Current methods in computer vision and object detection rely heavily on neural networks and deep learning. This active area of research is used in… (more)

Subjects/Keywords: Deep learning; Deformable bounding boxes; Object detection; YOLO

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APA (6th Edition):

Bhat, A. (2019). Aerial Object Detection using Learnable Bounding Boxes. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10203

Chicago Manual of Style (16th Edition):

Bhat, Aneesh. “Aerial Object Detection using Learnable Bounding Boxes.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed January 22, 2021. https://scholarworks.rit.edu/theses/10203.

MLA Handbook (7th Edition):

Bhat, Aneesh. “Aerial Object Detection using Learnable Bounding Boxes.” 2019. Web. 22 Jan 2021.

Vancouver:

Bhat A. Aerial Object Detection using Learnable Bounding Boxes. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Jan 22]. Available from: https://scholarworks.rit.edu/theses/10203.

Council of Science Editors:

Bhat A. Aerial Object Detection using Learnable Bounding Boxes. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10203


San Jose State University

4. Patira, Samkit. Over speed detection using Artificial Intelligence.

Degree: MS, Computer Science, 2019, San Jose State University

  Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one… (more)

Subjects/Keywords: speeding; computer vision; YOLO; Artificial Intelligence and Robotics

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APA (6th Edition):

Patira, S. (2019). Over speed detection using Artificial Intelligence. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.u8qc-9d6e ; https://scholarworks.sjsu.edu/etd_projects/712

Chicago Manual of Style (16th Edition):

Patira, Samkit. “Over speed detection using Artificial Intelligence.” 2019. Masters Thesis, San Jose State University. Accessed January 22, 2021. https://doi.org/10.31979/etd.u8qc-9d6e ; https://scholarworks.sjsu.edu/etd_projects/712.

MLA Handbook (7th Edition):

Patira, Samkit. “Over speed detection using Artificial Intelligence.” 2019. Web. 22 Jan 2021.

Vancouver:

Patira S. Over speed detection using Artificial Intelligence. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 Jan 22]. Available from: https://doi.org/10.31979/etd.u8qc-9d6e ; https://scholarworks.sjsu.edu/etd_projects/712.

Council of Science Editors:

Patira S. Over speed detection using Artificial Intelligence. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.u8qc-9d6e ; https://scholarworks.sjsu.edu/etd_projects/712


San Jose State University

5. Ordonia, Samuel. Detecting Cars in a Parking Lot using Deep Learning.

Degree: MS, Computer Science, 2019, San Jose State University

  Detection of cars in a parking lot with deep learning involves locating all objects of interest in a parking lot image and classifying the… (more)

Subjects/Keywords: parking lot images; Yolo; CNN; Deep Learning; Artificial Intelligence and Robotics

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APA (6th Edition):

Ordonia, S. (2019). Detecting Cars in a Parking Lot using Deep Learning. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.m6as-epyd ; https://scholarworks.sjsu.edu/etd_projects/696

Chicago Manual of Style (16th Edition):

Ordonia, Samuel. “Detecting Cars in a Parking Lot using Deep Learning.” 2019. Masters Thesis, San Jose State University. Accessed January 22, 2021. https://doi.org/10.31979/etd.m6as-epyd ; https://scholarworks.sjsu.edu/etd_projects/696.

MLA Handbook (7th Edition):

Ordonia, Samuel. “Detecting Cars in a Parking Lot using Deep Learning.” 2019. Web. 22 Jan 2021.

Vancouver:

Ordonia S. Detecting Cars in a Parking Lot using Deep Learning. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 Jan 22]. Available from: https://doi.org/10.31979/etd.m6as-epyd ; https://scholarworks.sjsu.edu/etd_projects/696.

Council of Science Editors:

Ordonia S. Detecting Cars in a Parking Lot using Deep Learning. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.m6as-epyd ; https://scholarworks.sjsu.edu/etd_projects/696


California State University – Sacramento

6. Amos, Vanna Mae. A historical account of people, resources and points of interest in early Yolo County for teacher use in enriching social studies.

Degree: MA, Education (Elementary Education, 2015, California State University – Sacramento

 For some time Yolo County schools have needed a source of historical data. for teaching and enriching the social studies in the elementary grades. Some… (more)

Subjects/Keywords: Social studies – Study and teaching; Yolo County (Calif.) – History

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APA (6th Edition):

Amos, V. M. (2015). A historical account of people, resources and points of interest in early Yolo County for teacher use in enriching social studies. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/140121

Chicago Manual of Style (16th Edition):

Amos, Vanna Mae. “A historical account of people, resources and points of interest in early Yolo County for teacher use in enriching social studies.” 2015. Masters Thesis, California State University – Sacramento. Accessed January 22, 2021. http://hdl.handle.net/10211.3/140121.

MLA Handbook (7th Edition):

Amos, Vanna Mae. “A historical account of people, resources and points of interest in early Yolo County for teacher use in enriching social studies.” 2015. Web. 22 Jan 2021.

Vancouver:

Amos VM. A historical account of people, resources and points of interest in early Yolo County for teacher use in enriching social studies. [Internet] [Masters thesis]. California State University – Sacramento; 2015. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10211.3/140121.

Council of Science Editors:

Amos VM. A historical account of people, resources and points of interest in early Yolo County for teacher use in enriching social studies. [Masters Thesis]. California State University – Sacramento; 2015. Available from: http://hdl.handle.net/10211.3/140121


Delft University of Technology

7. Bos, Evert (author). Including traffic light recognition in general object detection with YOLOv2.

Degree: 2019, Delft University of Technology

With an in vehicle camera many different things can be done that are essential for ADAS or autonomous driving mode in a vehicle. First, it… (more)

Subjects/Keywords: Traffic Light recognition; machine learning; YOLO; object detection; COCO; LISA

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APA (6th Edition):

Bos, E. (. (2019). Including traffic light recognition in general object detection with YOLOv2. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03

Chicago Manual of Style (16th Edition):

Bos, Evert (author). “Including traffic light recognition in general object detection with YOLOv2.” 2019. Masters Thesis, Delft University of Technology. Accessed January 22, 2021. http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03.

MLA Handbook (7th Edition):

Bos, Evert (author). “Including traffic light recognition in general object detection with YOLOv2.” 2019. Web. 22 Jan 2021.

Vancouver:

Bos E(. Including traffic light recognition in general object detection with YOLOv2. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03.

Council of Science Editors:

Bos E(. Including traffic light recognition in general object detection with YOLOv2. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03

8. Macário, Filipe Daniel Marques. Monitorização de passagens de nível com recurso a imagem digital .

Degree: 2019, Universidade de Aveiro

 Embora a massificação e a utilização de comboios tenha contribuído para um maior bem-estar por parte das populações, existem problemas inerentes, sendo um deles os… (more)

Subjects/Keywords: Monitorização; Deteção de obstáculos; Classificação de obstáculos; OpenCV; YOLO; Vídeo; Vigilância

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APA (6th Edition):

Macário, F. D. M. (2019). Monitorização de passagens de nível com recurso a imagem digital . (Thesis). Universidade de Aveiro. Retrieved from http://hdl.handle.net/10773/29672

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Macário, Filipe Daniel Marques. “Monitorização de passagens de nível com recurso a imagem digital .” 2019. Thesis, Universidade de Aveiro. Accessed January 22, 2021. http://hdl.handle.net/10773/29672.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Macário, Filipe Daniel Marques. “Monitorização de passagens de nível com recurso a imagem digital .” 2019. Web. 22 Jan 2021.

Vancouver:

Macário FDM. Monitorização de passagens de nível com recurso a imagem digital . [Internet] [Thesis]. Universidade de Aveiro; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10773/29672.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Macário FDM. Monitorização de passagens de nível com recurso a imagem digital . [Thesis]. Universidade de Aveiro; 2019. Available from: http://hdl.handle.net/10773/29672

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

9. Koščová, Zuzana. Konvoluční neuronové sítě pro detekci objektů v medicínských obrazech: Deep-learning-based pattern detection in medical images.

Degree: 2020, Brno University of Technology

 This Bachelor thesis deals with Deep-learning-based pattern detection in medical images. For better understanding of a subject artificial neural network and convolutional neural network (CNN)… (more)

Subjects/Keywords: konvolučné neurónové siete; detekcia objektov; CT; MRI; Faster R-CNN; YOLO; ohraničujúci rámček; pečeň; Deep learning; pattern detection; CT; MRI; Faster R-CNN; YOLO; bounding box; liver

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APA (6th Edition):

Koščová, Z. (2020). Konvoluční neuronové sítě pro detekci objektů v medicínských obrazech: Deep-learning-based pattern detection in medical images. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/190473

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Koščová, Zuzana. “Konvoluční neuronové sítě pro detekci objektů v medicínských obrazech: Deep-learning-based pattern detection in medical images.” 2020. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/190473.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Koščová, Zuzana. “Konvoluční neuronové sítě pro detekci objektů v medicínských obrazech: Deep-learning-based pattern detection in medical images.” 2020. Web. 22 Jan 2021.

Vancouver:

Koščová Z. Konvoluční neuronové sítě pro detekci objektů v medicínských obrazech: Deep-learning-based pattern detection in medical images. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/190473.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Koščová Z. Konvoluční neuronové sítě pro detekci objektů v medicínských obrazech: Deep-learning-based pattern detection in medical images. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/190473

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

10. Kočica, Filip. Detekce dopravních značek v obraze a videu: Detection of Traffic Signs in Image and Video.

Degree: 2019, Brno University of Technology

 This thesis deals with the traffic sign detection problematics using modern techniques in image processing. Special architecture of deep convolutional neural network YOLO, i.e. You… (more)

Subjects/Keywords: konvoluční neuronová síť; YOLO; detekce; klasifikace; syntetická; reálná; datová sada; dopravní značka; convolutional neural network; YOLO; detection; classification; synthetic; real; dataset; traffic sign

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APA (6th Edition):

Kočica, F. (2019). Detekce dopravních značek v obraze a videu: Detection of Traffic Signs in Image and Video. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180281

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kočica, Filip. “Detekce dopravních značek v obraze a videu: Detection of Traffic Signs in Image and Video.” 2019. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/180281.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kočica, Filip. “Detekce dopravních značek v obraze a videu: Detection of Traffic Signs in Image and Video.” 2019. Web. 22 Jan 2021.

Vancouver:

Kočica F. Detekce dopravních značek v obraze a videu: Detection of Traffic Signs in Image and Video. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/180281.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kočica F. Detekce dopravních značek v obraze a videu: Detection of Traffic Signs in Image and Video. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180281

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

11. Pitoňák, Radoslav. Deep Learning for Object Detection: Deep Learning for Object Detection.

Degree: 2019, Brno University of Technology

 This thesis analyzes different object detection methods which are based on deep neural networks. In the beginning, the convolutional neural networks are described and commonly… (more)

Subjects/Keywords: Detekcia objektov; hlboké neurónové siete; konvolučné neurónové siete; počítačové videnie; BDD; YOLO; Object detection; deep neural networks; convolutional neural networks; computer vision; BDD; YOLO

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APA (6th Edition):

Pitoňák, R. (2019). Deep Learning for Object Detection: Deep Learning for Object Detection. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180085

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pitoňák, Radoslav. “Deep Learning for Object Detection: Deep Learning for Object Detection.” 2019. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/180085.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pitoňák, Radoslav. “Deep Learning for Object Detection: Deep Learning for Object Detection.” 2019. Web. 22 Jan 2021.

Vancouver:

Pitoňák R. Deep Learning for Object Detection: Deep Learning for Object Detection. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/180085.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pitoňák R. Deep Learning for Object Detection: Deep Learning for Object Detection. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180085

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

12. Lukáč, Jakub. Sledování osob v záznamu z dronu: Tracking People in Video Captured from a Drone.

Degree: 2020, Brno University of Technology

 This thesis deals with the problem of determining the location of a person and its approximation. The location is derived from video which is captured… (more)

Subjects/Keywords: spracovanie obrazu; rozpoznávanie; rozpoznávanie objektov; sledovanie; odhad vzdialenosti; dron; Intel Movidius; INCS; Raspberry Pi; YOLO; image processing; image recognition; object detection; tracking; drone; distance estimation; Intel Movidius; Raspberry Pi; YOLO

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APA (6th Edition):

Lukáč, J. (2020). Sledování osob v záznamu z dronu: Tracking People in Video Captured from a Drone. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/192504

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lukáč, Jakub. “Sledování osob v záznamu z dronu: Tracking People in Video Captured from a Drone.” 2020. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/192504.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lukáč, Jakub. “Sledování osob v záznamu z dronu: Tracking People in Video Captured from a Drone.” 2020. Web. 22 Jan 2021.

Vancouver:

Lukáč J. Sledování osob v záznamu z dronu: Tracking People in Video Captured from a Drone. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/192504.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lukáč J. Sledování osob v záznamu z dronu: Tracking People in Video Captured from a Drone. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/192504

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


San Jose State University

13. MacKay, Charles Thane. Learning for Free – Object Detectors Trained on Synthetic Data.

Degree: MS, Computer Science, 2019, San Jose State University

  A picture is worth a thousand words, or if you want it labeled, it’s worth about four cents per bounding box. Data is the… (more)

Subjects/Keywords: sythetic data generation; image video data; YOLO; GAN; Artificial Intelligence and Robotics

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APA (6th Edition):

MacKay, C. T. (2019). Learning for Free – Object Detectors Trained on Synthetic Data. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.e4vb-qdwk ; https://scholarworks.sjsu.edu/etd_projects/738

Chicago Manual of Style (16th Edition):

MacKay, Charles Thane. “Learning for Free – Object Detectors Trained on Synthetic Data.” 2019. Masters Thesis, San Jose State University. Accessed January 22, 2021. https://doi.org/10.31979/etd.e4vb-qdwk ; https://scholarworks.sjsu.edu/etd_projects/738.

MLA Handbook (7th Edition):

MacKay, Charles Thane. “Learning for Free – Object Detectors Trained on Synthetic Data.” 2019. Web. 22 Jan 2021.

Vancouver:

MacKay CT. Learning for Free – Object Detectors Trained on Synthetic Data. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 Jan 22]. Available from: https://doi.org/10.31979/etd.e4vb-qdwk ; https://scholarworks.sjsu.edu/etd_projects/738.

Council of Science Editors:

MacKay CT. Learning for Free – Object Detectors Trained on Synthetic Data. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.e4vb-qdwk ; https://scholarworks.sjsu.edu/etd_projects/738


Grand Valley State University

14. Aryal, Milan. Object Detection, Classification, and Tracking for Autonomous Vehicle.

Degree: 2018, Grand Valley State University

 The detection and tracking of objects around an autonomous vehicle is essential to operate safely. This paper presents an algorithm to detect, classify, and track… (more)

Subjects/Keywords: Object Detection; Tracking; EKF; SLAM; YOLO; Automotive Engineering; Navigation, Guidance, Control, and Dynamics

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APA (6th Edition):

Aryal, M. (2018). Object Detection, Classification, and Tracking for Autonomous Vehicle. (Thesis). Grand Valley State University. Retrieved from https://scholarworks.gvsu.edu/theses/912

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Aryal, Milan. “Object Detection, Classification, and Tracking for Autonomous Vehicle.” 2018. Thesis, Grand Valley State University. Accessed January 22, 2021. https://scholarworks.gvsu.edu/theses/912.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Aryal, Milan. “Object Detection, Classification, and Tracking for Autonomous Vehicle.” 2018. Web. 22 Jan 2021.

Vancouver:

Aryal M. Object Detection, Classification, and Tracking for Autonomous Vehicle. [Internet] [Thesis]. Grand Valley State University; 2018. [cited 2021 Jan 22]. Available from: https://scholarworks.gvsu.edu/theses/912.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Aryal M. Object Detection, Classification, and Tracking for Autonomous Vehicle. [Thesis]. Grand Valley State University; 2018. Available from: https://scholarworks.gvsu.edu/theses/912

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Cal Poly

15. Venkatesh, Anirudh. Object Tracking in Games using Convolutional Neural Networks.

Degree: MS, Computer Science, 2018, Cal Poly

  Computer vision research has been growing rapidly over the last decade. Recent advancements in the field have been widely used in staple products across… (more)

Subjects/Keywords: Convolutional Neural Networks; YOLO; Games; CNNs; Neural Networks; Object Detection; Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Venkatesh, A. (2018). Object Tracking in Games using Convolutional Neural Networks. (Masters Thesis). Cal Poly. Retrieved from https://digitalcommons.calpoly.edu/theses/1845 ; 10.15368/theses.2018.56

Chicago Manual of Style (16th Edition):

Venkatesh, Anirudh. “Object Tracking in Games using Convolutional Neural Networks.” 2018. Masters Thesis, Cal Poly. Accessed January 22, 2021. https://digitalcommons.calpoly.edu/theses/1845 ; 10.15368/theses.2018.56.

MLA Handbook (7th Edition):

Venkatesh, Anirudh. “Object Tracking in Games using Convolutional Neural Networks.” 2018. Web. 22 Jan 2021.

Vancouver:

Venkatesh A. Object Tracking in Games using Convolutional Neural Networks. [Internet] [Masters thesis]. Cal Poly; 2018. [cited 2021 Jan 22]. Available from: https://digitalcommons.calpoly.edu/theses/1845 ; 10.15368/theses.2018.56.

Council of Science Editors:

Venkatesh A. Object Tracking in Games using Convolutional Neural Networks. [Masters Thesis]. Cal Poly; 2018. Available from: https://digitalcommons.calpoly.edu/theses/1845 ; 10.15368/theses.2018.56


AUT University

16. Fu, Yuhang. Fruit Freshness Grading Using Deep Learning .

Degree: AUT University

 This thesis presents a comprehensive analysis of a variety of fruit images for freshness grading using deep learning. A number of algorithms have been reviewed… (more)

Subjects/Keywords: CNN; YOLO; Deep Learning; Fruit Freshness; Regression; Image Recognition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Fu, Y. (n.d.). Fruit Freshness Grading Using Deep Learning . (Thesis). AUT University. Retrieved from http://hdl.handle.net/10292/13353

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Fu, Yuhang. “Fruit Freshness Grading Using Deep Learning .” Thesis, AUT University. Accessed January 22, 2021. http://hdl.handle.net/10292/13353.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Fu, Yuhang. “Fruit Freshness Grading Using Deep Learning .” Web. 22 Jan 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Fu Y. Fruit Freshness Grading Using Deep Learning . [Internet] [Thesis]. AUT University; [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10292/13353.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Fu Y. Fruit Freshness Grading Using Deep Learning . [Thesis]. AUT University; Available from: http://hdl.handle.net/10292/13353

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

17. Güven, Jakup. Investigating techniques for improving accuracy and limiting overfitting for YOLO and real-time object detection on iOS.

Degree: Faculty of Technology and Society (TS), 2019, Malmö University

I detta arbete genomförs utvecklingen av ett realtids objektdetekteringssystem för iOS. För detta ändamål används YOLO, en ett-stegs objektdetekterare och ett s.k. ihoplänkat neuralt… (more)

Subjects/Keywords: YOLO; object detection; overfitting; dataset composition; hyperparameter optimisation; transfer learning; iOS; real-time; improving accuracy; Engineering and Technology; Teknik och teknologier

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Güven, J. (2019). Investigating techniques for improving accuracy and limiting overfitting for YOLO and real-time object detection on iOS. (Thesis). Malmö University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-19999

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Güven, Jakup. “Investigating techniques for improving accuracy and limiting overfitting for YOLO and real-time object detection on iOS.” 2019. Thesis, Malmö University. Accessed January 22, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-19999.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Güven, Jakup. “Investigating techniques for improving accuracy and limiting overfitting for YOLO and real-time object detection on iOS.” 2019. Web. 22 Jan 2021.

Vancouver:

Güven J. Investigating techniques for improving accuracy and limiting overfitting for YOLO and real-time object detection on iOS. [Internet] [Thesis]. Malmö University; 2019. [cited 2021 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-19999.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Güven J. Investigating techniques for improving accuracy and limiting overfitting for YOLO and real-time object detection on iOS. [Thesis]. Malmö University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-19999

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. Wetzel, William Thomas. Reducing general fund expenditures stormwater in West Sacramento, California.

Degree: M.P.P.A., Public Policy and Administration, 2013, California State University – Sacramento

 Nobody wants to repeat Hurricane Katrina???s aftermath. However, even in a region historically prone to flooding, the City of West Sacramento must provide more than… (more)

Subjects/Keywords: Municipal finance; Utility; LAFCO; Yolo; MSR

…water into the Sacramento River to the east, the Yolo Bypass to the north, and the Main Ship… …of these alternatives while others are from Yolo Local Agency Formation Commission (… …LAFCO) documents discussing water in Yolo County. Then I identify and explain my criteria… …provided service (Yolo County LAFCO, 2011). 15 Although reclamation districts have a… …that may affect service delivery in the future. The options that the Yolo LAFCO identified in… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wetzel, W. T. (2013). Reducing general fund expenditures stormwater in West Sacramento, California. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.9/2039

Chicago Manual of Style (16th Edition):

Wetzel, William Thomas. “Reducing general fund expenditures stormwater in West Sacramento, California.” 2013. Masters Thesis, California State University – Sacramento. Accessed January 22, 2021. http://hdl.handle.net/10211.9/2039.

MLA Handbook (7th Edition):

Wetzel, William Thomas. “Reducing general fund expenditures stormwater in West Sacramento, California.” 2013. Web. 22 Jan 2021.

Vancouver:

Wetzel WT. Reducing general fund expenditures stormwater in West Sacramento, California. [Internet] [Masters thesis]. California State University – Sacramento; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10211.9/2039.

Council of Science Editors:

Wetzel WT. Reducing general fund expenditures stormwater in West Sacramento, California. [Masters Thesis]. California State University – Sacramento; 2013. Available from: http://hdl.handle.net/10211.9/2039

19. Cardenas, Kathryn Anne. Social media use in local government: an implementation guide for public officials.

Degree: M.P.P.A., Public Policy and Administration, 2013, California State University – Sacramento

 Due to the popularity of social media among the general populous, there is potential for it to be a useful tool for local government agencies.… (more)

Subjects/Keywords: Facebook; Social networking; Policies; Strategies; Yolo county

…35 3. SOCIAL MEDIA IN YOLO COUNTY… …36 Yolo County… …36 Yolo County’s Use of Social Media… …39 My Internship in Yolo County… …42 Yolo County’s Organizational Context… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cardenas, K. A. (2013). Social media use in local government: an implementation guide for public officials. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.9/2234

Chicago Manual of Style (16th Edition):

Cardenas, Kathryn Anne. “Social media use in local government: an implementation guide for public officials.” 2013. Masters Thesis, California State University – Sacramento. Accessed January 22, 2021. http://hdl.handle.net/10211.9/2234.

MLA Handbook (7th Edition):

Cardenas, Kathryn Anne. “Social media use in local government: an implementation guide for public officials.” 2013. Web. 22 Jan 2021.

Vancouver:

Cardenas KA. Social media use in local government: an implementation guide for public officials. [Internet] [Masters thesis]. California State University – Sacramento; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10211.9/2234.

Council of Science Editors:

Cardenas KA. Social media use in local government: an implementation guide for public officials. [Masters Thesis]. California State University – Sacramento; 2013. Available from: http://hdl.handle.net/10211.9/2234


Uppsala University

20. Melcherson, Tim. Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model.

Degree: Signals and Systems, 2020, Uppsala University

  Research in the Arctic is of ever growing importance, and modern technology is used in news ways to map and understand this very complex… (more)

Subjects/Keywords: Artificial Intelligence; Image Processing; Convolutional Neural Networks; Deep Learning; YOLO; YOLOv4; Artificiell Intelligens; Bildbehandling; neurala nätverk; djupinlärning; Computer Systems; Datorsystem

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Melcherson, T. (2020). Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Melcherson, Tim. “Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model.” 2020. Thesis, Uppsala University. Accessed January 22, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Melcherson, Tim. “Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model.” 2020. Web. 22 Jan 2021.

Vancouver:

Melcherson T. Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model. [Internet] [Thesis]. Uppsala University; 2020. [cited 2021 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Melcherson T. Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model. [Thesis]. Uppsala University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

21. Sladký, Jiří. Vizuální detekce malých předmětů pomocí dostupných nástrojů v prostředí MATLAB: Visual detection of small objects using available tools in MATLAB.

Degree: 2020, Brno University of Technology

 This thesis investigates possibilities of small object detection in pictures using YOLO method, a deep learning algorithm available in MATLAB. In the thesis, a detector… (more)

Subjects/Keywords: Strojové učení; hluboké učení; počítačové vidění; umělé neuronové sítě; detekce objektů; YOLO; syntetický generátor; augmentace dat; detekce dobytka; machine learning; deep learning; computer vision; artificial neural networks; object detection; YOLO; synthetic generator; data augmentation; cattle detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sladký, J. (2020). Vizuální detekce malých předmětů pomocí dostupných nástrojů v prostředí MATLAB: Visual detection of small objects using available tools in MATLAB. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/193473

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sladký, Jiří. “Vizuální detekce malých předmětů pomocí dostupných nástrojů v prostředí MATLAB: Visual detection of small objects using available tools in MATLAB.” 2020. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/193473.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sladký, Jiří. “Vizuální detekce malých předmětů pomocí dostupných nástrojů v prostředí MATLAB: Visual detection of small objects using available tools in MATLAB.” 2020. Web. 22 Jan 2021.

Vancouver:

Sladký J. Vizuální detekce malých předmětů pomocí dostupných nástrojů v prostředí MATLAB: Visual detection of small objects using available tools in MATLAB. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/193473.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sladký J. Vizuální detekce malých předmětů pomocí dostupných nástrojů v prostředí MATLAB: Visual detection of small objects using available tools in MATLAB. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/193473

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

22. Chocholatý, Tomáš. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.

Degree: 2020, Brno University of Technology

 The thesis focuses on traffic sign detection and traffic lights detection in view with utilization convolution neural network. The goal is create suitable detector for… (more)

Subjects/Keywords: Detekce a klasifikace dopravní značek; konvoluční neuronové sítě; detekce objektů v obraze; YOLO; syntetická datová sada; generátor syntetické datové sady; kvantitativní vyhodnocování; Traffic sign detection and classification; Convolution neural network; Object detecion; YOLO; Synthetic dataset; Generator for synthetic dataset; Quantitative evaluation

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APA (6th Edition):

Chocholatý, T. (2020). Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/189824

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chocholatý, Tomáš. “Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.” 2020. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/189824.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chocholatý, Tomáš. “Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.” 2020. Web. 22 Jan 2021.

Vancouver:

Chocholatý T. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/189824.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chocholatý T. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/189824

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

23. Pavlica, Jan. Detekce graffiti tagů v obraze: Detection of Graffiti Tags in Image.

Degree: 2019, Brno University of Technology

 The thesis is focused on the possible utilization of current methods in the area of computer vision with the purpose of automatic detection of graffiti… (more)

Subjects/Keywords: graffiti tagy; detekce objektů; konvoluční neuronové sítě; YOLOv2; Tiny YOLO; You Look Only Once; SSD; Single Shot MultiBox Detector; Faster R-CNN; graffiti tags; object detection; convolutional neural networks; YOLOv2; Tiny YOLO; You Look Only Once; SSD; Single Shot MultiBox Detector; Faster R-CNN

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pavlica, J. (2019). Detekce graffiti tagů v obraze: Detection of Graffiti Tags in Image. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/69645

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pavlica, Jan. “Detekce graffiti tagů v obraze: Detection of Graffiti Tags in Image.” 2019. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/69645.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pavlica, Jan. “Detekce graffiti tagů v obraze: Detection of Graffiti Tags in Image.” 2019. Web. 22 Jan 2021.

Vancouver:

Pavlica J. Detekce graffiti tagů v obraze: Detection of Graffiti Tags in Image. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/69645.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pavlica J. Detekce graffiti tagů v obraze: Detection of Graffiti Tags in Image. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/69645

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

24. Líbal, Tomáš. Detekce registrační značky vozidla ve videu: Detection of Vehicle License Plates in Video.

Degree: 2019, Brno University of Technology

 This thesis deals with preparation of training dataset and training of convolutional neural network for licence plate detection in video. Darknet technology was used for… (more)

Subjects/Keywords: Konvoluční neuronová síť; CNN; Hluboké učení; Darknet; YOLO; YOLOv3; YOLOv3-tiny; detekce registračních značek; detekce objektů; ROC; Convolutional neural network; CNN; Deep learning; Darknet; YOLO; YOLOv3; YOLOv3-tiny; licence plate detection; Object Detection; ROC

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APA (6th Edition):

Líbal, T. (2019). Detekce registrační značky vozidla ve videu: Detection of Vehicle License Plates in Video. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180101

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Líbal, Tomáš. “Detekce registrační značky vozidla ve videu: Detection of Vehicle License Plates in Video.” 2019. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/180101.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Líbal, Tomáš. “Detekce registrační značky vozidla ve videu: Detection of Vehicle License Plates in Video.” 2019. Web. 22 Jan 2021.

Vancouver:

Líbal T. Detekce registrační značky vozidla ve videu: Detection of Vehicle License Plates in Video. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/180101.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Líbal T. Detekce registrační značky vozidla ve videu: Detection of Vehicle License Plates in Video. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180101

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

25. Válek, Lukáš. Automatická analýza scény v dopravě prostřednictvím zpracování obrazu: Automatic Trafic Scene Analysis Using Image Processing.

Degree: 2020, Brno University of Technology

 This thesis deals with the issue of scene analysis using computer vision methods. The aim of this work is to create a system capable of… (more)

Subjects/Keywords: Rozpoznávání obrazu; Strojové učení; Python; Flask; YOLO; DeepSort; Webová aplikace; Detekce objektů; Sledování objektů; Počítačové Vidění; Image Recognition; Machine Learning; Python; Flask; YOLO; DeepSort; Web Application; Object detection; Object tracking; Computer Vision

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APA (6th Edition):

Válek, L. (2020). Automatická analýza scény v dopravě prostřednictvím zpracování obrazu: Automatic Trafic Scene Analysis Using Image Processing. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/191404

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Válek, Lukáš. “Automatická analýza scény v dopravě prostřednictvím zpracování obrazu: Automatic Trafic Scene Analysis Using Image Processing.” 2020. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/191404.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Válek, Lukáš. “Automatická analýza scény v dopravě prostřednictvím zpracování obrazu: Automatic Trafic Scene Analysis Using Image Processing.” 2020. Web. 22 Jan 2021.

Vancouver:

Válek L. Automatická analýza scény v dopravě prostřednictvím zpracování obrazu: Automatic Trafic Scene Analysis Using Image Processing. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/191404.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Válek L. Automatická analýza scény v dopravě prostřednictvím zpracování obrazu: Automatic Trafic Scene Analysis Using Image Processing. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/191404

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

26. Červíček, Petr. Webová aplikace pro kontrolu výsledků automatického zpracování videa a jeho ručního anotování: Web Application for Inspecting Results of Automatic Video Processing and Manual Annotations.

Degree: 2020, Brno University of Technology

 The thesis pursues the implementation of the web application for obtaining valuable data for anotation. Data are mainly collected from videos and images, but they… (more)

Subjects/Keywords: web; webová aplikace; single page aplikace; YOLO; anotování; video; obrázky; deepfakes; Node; React; Material-UI; detekce objektů; sledování objektů; web; web application; single page application; YOLO; annotation; video; images; deepfakes; Node; React; Material-UI; object detection; object tracking

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Červíček, P. (2020). Webová aplikace pro kontrolu výsledků automatického zpracování videa a jeho ručního anotování: Web Application for Inspecting Results of Automatic Video Processing and Manual Annotations. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/191434

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Červíček, Petr. “Webová aplikace pro kontrolu výsledků automatického zpracování videa a jeho ručního anotování: Web Application for Inspecting Results of Automatic Video Processing and Manual Annotations.” 2020. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/191434.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Červíček, Petr. “Webová aplikace pro kontrolu výsledků automatického zpracování videa a jeho ručního anotování: Web Application for Inspecting Results of Automatic Video Processing and Manual Annotations.” 2020. Web. 22 Jan 2021.

Vancouver:

Červíček P. Webová aplikace pro kontrolu výsledků automatického zpracování videa a jeho ručního anotování: Web Application for Inspecting Results of Automatic Video Processing and Manual Annotations. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/191434.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Červíček P. Webová aplikace pro kontrolu výsledků automatického zpracování videa a jeho ručního anotování: Web Application for Inspecting Results of Automatic Video Processing and Manual Annotations. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/191434

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

27. Akhtar, Muhammad Bilal. Optical Inspection for Soldering Fault Detection in a PCB Assembly using Convolutional Neural Networks.

Degree: Electrical Engineering and Computer Science (EECS), 2019, KTH

Convolutional Neural Network (CNN) has been established as a powerful toolto automate various computer vision tasks without requiring any aprioriknowledge. Printed Circuit Board (PCB)… (more)

Subjects/Keywords: Automatic Optical Inspection; Deep Learning; Convolutional Neural Networks; Object Detection; Soldering Bridge Fault; YOLO; Optimization; Automatisk optisk inspektion; Djup lärning; Konvolutional Neural Network; Objektdetektion; Lödbronfel; YOLO; Optimering; Engineering and Technology; Teknik och teknologier

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Akhtar, M. B. (2019). Optical Inspection for Soldering Fault Detection in a PCB Assembly using Convolutional Neural Networks. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-270703

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Akhtar, Muhammad Bilal. “Optical Inspection for Soldering Fault Detection in a PCB Assembly using Convolutional Neural Networks.” 2019. Thesis, KTH. Accessed January 22, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-270703.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Akhtar, Muhammad Bilal. “Optical Inspection for Soldering Fault Detection in a PCB Assembly using Convolutional Neural Networks.” 2019. Web. 22 Jan 2021.

Vancouver:

Akhtar MB. Optical Inspection for Soldering Fault Detection in a PCB Assembly using Convolutional Neural Networks. [Internet] [Thesis]. KTH; 2019. [cited 2021 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-270703.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Akhtar MB. Optical Inspection for Soldering Fault Detection in a PCB Assembly using Convolutional Neural Networks. [Thesis]. KTH; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-270703

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

28. Chocholatý, Tomáš. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.

Degree: 2020, Brno University of Technology

 The thesis focuses on traffic sign detection and traffic lights detection in view with utilization convolution neural network. The goal is create suitable detector for… (more)

Subjects/Keywords: Detekce a klasifikace dopravní značek; konvoluční neuronové sítě; detekce objektů v obraze; YOLO; syntetická datová sada; generátor syntetické datové sady; kvantitativní vyhodnocování; Traffic sign detection and classification; Convolution neural network; Object detecion; YOLO; Synthetic dataset; Generator for synthetic dataset; Quantitative evaluation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chocholatý, T. (2020). Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/188579

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chocholatý, Tomáš. “Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.” 2020. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/188579.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chocholatý, Tomáš. “Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.” 2020. Web. 22 Jan 2021.

Vancouver:

Chocholatý T. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/188579.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chocholatý T. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/188579

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

29. Chocholatý, Tomáš. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.

Degree: 2019, Brno University of Technology

 The thesis focuses on traffic sign detection and traffic lights detection in view with utilization convolution neural network. The goal is create suitable detector for… (more)

Subjects/Keywords: Detekce a klasifikace dopravní značek; konvoluční neuronové sítě; detekce objektů v obraze; YOLO; syntetická datová sada; generátor syntetické datové sady; kvantitativní vyhodnocování; Traffic sign detection and classification; Convolution neural network; Object detecion; YOLO; Synthetic dataset; Generator for synthetic dataset; Quantitative evaluation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chocholatý, T. (2019). Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180112

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chocholatý, Tomáš. “Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.” 2019. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/180112.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chocholatý, Tomáš. “Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.” 2019. Web. 22 Jan 2021.

Vancouver:

Chocholatý T. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/180112.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chocholatý T. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180112

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brno University of Technology

30. Voronin, Artyom. Klasifikace dat v obraze pomocí nástrojů pro strojové učení v jazyce Python.

Degree: 2019, Brno University of Technology

 This thesis introduces the issue of data classification in the image using tools for machine learning in Python. The aim is to verify the possibilities… (more)

Subjects/Keywords: Python; Strojově učení; Hloubkově učení; Konvoluční neuronové sítě; Faster-R-CNN; SSD-Mobilenet; YOLO; Tensorflow; Počítačová vize; OpenCV; Mechatronika; Python; Machine Learning; Deep Learning; Convolution Neural Networks; Faster-R-CNN; SSD-Mobilenet; YOLO; Tensorflow; Computer vision; OpenCV; Mechatronics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Voronin, A. (2019). Klasifikace dat v obraze pomocí nástrojů pro strojové učení v jazyce Python. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/179338

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Voronin, Artyom. “Klasifikace dat v obraze pomocí nástrojů pro strojové učení v jazyce Python.” 2019. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/179338.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Voronin, Artyom. “Klasifikace dat v obraze pomocí nástrojů pro strojové učení v jazyce Python.” 2019. Web. 22 Jan 2021.

Vancouver:

Voronin A. Klasifikace dat v obraze pomocí nástrojů pro strojové učení v jazyce Python. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/179338.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Voronin A. Klasifikace dat v obraze pomocí nástrojů pro strojové učení v jazyce Python. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/179338

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

[1] [2]

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