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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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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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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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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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Improving Accuracy of Detection and Recognition of Traffic Signs with GANs
Glos, Michal ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
The goal of this thesis was to extend a dataset for traffic sign detection. The solution was based on generative neural networks PatchGAN and Wasserstein GAN of combined DenseNet and U-Net architecture. Those models were designed to synthesize real looking traffic signs from images of their norms. Model for object detection SSD, trained on synthetic data only, achieved mean average precision of 59.6 %, which is an improvement of 9.4 % over the model trained on the original data. SSD model trained on synthetic and original data combined achieved mean average precision of 80.1 %.
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Design of traffic sign detector using image processing methods
Šmíd, Josef ; Adámek, Roman (referee) ; Věchet, Stanislav (advisor)
This master thesis deals with the design of a traffic sign detector using the image processing methods. The OpenCV library for working with images in programming language Python is used for this. The first part reports on the using methods. In the next part, these methods were tested on images of traffic signs taken in traffic in different lighting conditions. The results of these tests led to the design of optimal methods and their settings, which were re-verified by verifying on video of driving in traffic. This also revealed the conditions under which they can operate in real-time systems. Finally, an optimization algorithm for compensation of detection errors was proposed from the monitoring of detection waveforms.
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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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Detection of traffic signs for autonomous vehicles
Kovaříková, Lucie ; Píštěk, Václav (referee) ; Kučera, Pavel (advisor)
This master's thesis focuses on traffic sign detection for autonomous vehicles using the Python programming language and the YOLOv7 architecture of convolutional neural network. The objective is to conduct research in the field of traffic sign recognition and implement algorithms for camera communication and detection. The results include experimental verification of the detection system and its subsequent evaluation.
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Improving Accuracy of Detection and Recognition of Traffic Signs with GANs
Glos, Michal ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
The goal of this thesis was to extend a dataset for traffic sign detection. The solution was based on generative neural networks PatchGAN and Wasserstein GAN of combined DenseNet and U-Net architecture. Those models were designed to synthesize real looking traffic signs from images of their norms. Model for object detection SSD, trained on synthetic data only, achieved mean average precision of 59.6 %, which is an improvement of 9.4 % over the model trained on the original data. SSD model trained on synthetic and original data combined achieved mean average precision of 80.1 %.
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Design of traffic sign detector using image processing methods
Šmíd, Josef ; Adámek, Roman (referee) ; Věchet, Stanislav (advisor)
This master thesis deals with the design of a traffic sign detector using the image processing methods. The OpenCV library for working with images in programming language Python is used for this. The first part reports on the using methods. In the next part, these methods were tested on images of traffic signs taken in traffic in different lighting conditions. The results of these tests led to the design of optimal methods and their settings, which were re-verified by verifying on video of driving in traffic. This also revealed the conditions under which they can operate in real-time systems. Finally, an optimization algorithm for compensation of detection errors was proposed from the monitoring of detection waveforms.
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