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Generating training data with neural networks
Ševčík, Pavel ; Kolář, Martin (referee) ; Hradiš, Michal (advisor)
The aim of this thesis was to prepare a training data set for traffic sign detection using generative neural networks. The solution uses a modified U-Net architecture and several experiments with the application of styles using AdaIN layers as in the StyleGAN model have been conducted. By extending the real GTSDB data set with the generated images, mean average precision of 80.36 % has been achieved, which yields an improvement of 19.27 % compared to the mean average precision of the detection model trained on real data only.
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Deep Learning for Object Detection
Paníček, Andrej ; Herout, Adam (referee) ; Teuer, Lukáš (advisor)
This work deals with the object detection using deep neural networks. As part of the solution, I modified, implemented and trained the well-known model of cascade neural networks MTCNN so that it could perform the detection of traffic signs. The training data was generated from GTSRB and GTSDB data sets. MTCNN showed solid performance on the evaluation data, where the detection accuracy reached 97.8 %.
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Vehicle On-Board Camera Analysis
Kadeřábek, Jan ; Bartl, Vojtěch (referee) ; Špaňhel, Jakub (advisor)
This thesis focuses on analysis of video from vehicle on-board camera. During the process of analysis, probihibitory traffic signs are detected and their specific type is classified. For recognized speed limit signs, their numeric value is extracted. From the processed information, it will try to create a file containing the unique occurrences of traffic signs including their GPS coordinates. For the purpose of detection and recognition of traffic signs, several data sets were created. A~cascade classifier with LBP features is used as a detector. Classification of the type and value of traffic signs is done using the k-Nearest Neigbour method.
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Traffic Signs Detection
Ťapuška, Tomáš ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This bachelor's thesis is about traffic sign detection in picture. There are written some known methods, their advantages and disadvantages. There is present implementation of the system for traffic sign detection. There are present in the last chapter some tests that were done on the system with using testing set, which was created specialy for this purpose.
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Pattern recognition
Pelc, Matěj ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
This paper proposes robust algorithm for detection of traffic signs in well light conditional. The algorithm uses colour based segmentation method for finding red traffic signs. Fast radial symmetry method FRS is used for identification of constituent shapes. Traffic signs are divided into four classes on the basis of the method.
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