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Detection of Traffic Signs and Lights
Oškera, Jan ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
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.
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Advanced analysis of moving objects in transport
Hora, Adam ; Dejdar, Petr (referee) ; Kiac, Martin (advisor)
This thesis solves the problem of monitoring objects from live streams or camera recordings. The aim is also to create your own data set usable in solving traffic situations and analysis for object recognition and classification. The YOLO method with OpenCV support was used for evaluation purposes. The result is a program in which road recordings can be inserted or live broadcasts can be used from a camera positioned so that it captures the road. The output of the program is to find out the number of motor vehicles at any given moment and the average number of vehicles that were on the road during given periods of time. The videos from which the data set is created were provided by the thesis supervisor. The main benefit of this work is the ability to monitor traffic density at given time intervals.
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Advanced analysis of moving objects in transport
Hora, Adam ; Dejdar, Petr (referee) ; Kiac, Martin (advisor)
This thesis solves the problem of monitoring objects from live streams or camera recordings. The aim is also to create your own data set usable in solving traffic situations and analysis for object recognition and classification. The YOLO method with OpenCV support was used for evaluation purposes. The result is a program in which road recordings can be inserted or live broadcasts can be used from a camera positioned so that it captures the road. The output of the program is to find out the number of motor vehicles at any given moment and the average number of vehicles that were on the road during given periods of time. The videos from which the data set is created were provided by the thesis supervisor. The main benefit of this work is the ability to monitor traffic density at given time intervals.
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Advanced analysis of moving objects in transport
Hora, Adam ; Dejdar, Petr (referee) ; Kiac, Martin (advisor)
This thesis solves the problem of monitoring objects from live streams or camera recordings. The aim is also to create your own data set usable in solving traffic situations and analysis for object recognition and classification. The YOLO method with OpenCV support was used for evaluation purposes. The result is a program in which road recordings can be inserted or live broadcasts can be used from a camera positioned so that it captures the road. The output of the program is to find out the number of motor vehicles at any given moment and the average number of vehicles that were on the road during given periods of time. The videos from which the data set is created were provided by the thesis supervisor. The main benefit of this work is the ability to monitor traffic density at given time intervals.
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|
|
Detection of Traffic Signs and Lights
Oškera, Jan ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
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.
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