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Brno University of Technology

1. Oškera, Jan. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.

Degree: 2020, Brno University of Technology

The thesis focuses on modern methods of traffic sign detection and traffic lights detection directly in traffic and with use of back analysis. The main subject is convolutional neural networks (CNN). The solution is using convolutional neural networks of YOLO type. The main goal of this thesis is to achieve the greatest possible optimization of speed and accuracy of models. Examines suitable datasets. A number of datasets are used for training and testing. These are composed of real and synthetic data sets. For training and testing, the data were preprocessed using the Yolo mark tool. The training of the model was carried out at a computer center belonging to the virtual organization MetaCentrum VO. Due to the quantifiable evaluation of the detector quality, a program was created statistically and graphically showing its success with use of ROC curve and evaluation protocol COCO. In this thesis I created a model that achieved a success average rate of up to 81 %. The thesis shows the best choice of threshold across versions, sizes and IoU. Extension for mobile phones in TensorFlow Lite and Flutter have also been created. Advisors/Committee Members: Herout, Adam (advisor), Špaňhel, Jakub (referee).

Subjects/Keywords: YOLO; YOLOv3; YOLOv2-tiny; YOLOv3-tiny; YOLOv4-tiny; optimalizace; CNN; kon- voluční neuronové sítě; detekce objektů v obraze; MetaCentrum; TensorFlow Lite; Flutter; mobilní detektor; rozdělení datasetu; syntetický dataset; vstupní velikost; nejlepší threshold; dopravní datové sady; TDS.; YOLO; YOLOv3; YOLOv2-tiny; YOLOv3-tiny; YOLOv4-tiny; optimization; CNN; convo- lutional neural networks; object detection in the image; MetaCentrum; TensorFlow Lite; flutter; mobile detector; dataset distribution; synthetic dataset; input size; threshold; traffic data set; TDS.

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

APA (6th Edition):

Oškera, J. (2020). Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/195007

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):

Oškera, Jan. “Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.” 2020. Thesis, Brno University of Technology. Accessed January 26, 2021. http://hdl.handle.net/11012/195007.

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

MLA Handbook (7th Edition):

Oškera, Jan. “Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights.” 2020. Web. 26 Jan 2021.

Vancouver:

Oškera J. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 26]. Available from: http://hdl.handle.net/11012/195007.

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

Council of Science Editors:

Oškera J. Detekce dopravních značek a semaforů: Detection of Traffic Signs and Lights. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/195007

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

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