AUTOMATIC DETECTION AND RECOGNITION OF ROAD SIGNS USING CONVOLUTIONAL NEURAL NETWORKS
ЖОЛ БЕЛГІЛЕРІН АВТОМАТТЫ ТҮРДЕ КОНВОЛЮЦИЯЛЫҚ НЕЙРОНДЫҚ ЖЕЛІЛЕР КӨМЕГІМЕН АНЫҚТАУ ЖӘНЕ ТАНУ
АВТОМАТИЧЕСКОЕ ОБНАРУЖЕНИЕ И РАСПОЗНАВАНИЕ ДОРОЖНЫХ ЗНАКОВ С ПОМОЩЬЮ СВЕРТОЧНЫХ НЕЙРОННЫХ СЕТЕЙ
Omarov B.S. Ziyatbekova G.Z. Batyr Z.A. Mailybayeva A.D. Bydakhmet Z. Shametova G.K. Wójcik W.
2024Kazakh-British Technical University
Herald of the Kazakh British Technical UNiversity
2024#21Issue 481 - 90 pp.
This paper examines the use of convolutional neural networks (CNNs) to improve traffic sign recognition systems, precisely in non-weather conditions. An extended dataset of the German Traffic Sign Recognition Test (GTSRB), based on a new CNN model, is also used, which contains more than fifty thousand labeled images covering more than forty categories. The model presents adaptive object selection layers designed to eliminate visibility problems caused by weather factors such as rain, fog, and snow. Advanced data augmentation techniques are applied to model different weather scenarios, which increases the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. This study not only examines the theoretical and practical improvements provided by CNNs for traffic sign detection in unfavorable conditions, but also verifies the effectiveness of the model through metrics such as accuracy, responsiveness, and F1 score. The results confirm the effectiveness of the model in minimizing false positives and accurately identifying traffic signs. The paper emphasizes the importance of careful dataset preparation, model optimization and improved training to enhance the performance of the detection system. This has positive implications for intelligent transportation systems, autonomous driving and road safety, indicating future progress in robust traffic sign recognition technologies.
artificial intelligence , classification , CNN , image analysis , traffic sign detection
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Al-Farabi Kazakh National University, Almaty, Kazakhstan
Institute of Information and Computational Technologies SC MSHE RK, Almaty, Kazakhstan
Khalel Dosmukhamedov Atyrau University, Atyrau, Kazakhstan
Almaty University of Power Engineering and Telecommunications after Gumarbek Daukeev, Almaty, Kazakhstan
Lublin Technical University, Lublin, Poland
Al-Farabi Kazakh National University
Institute of Information and Computational Technologies SC MSHE RK
Khalel Dosmukhamedov Atyrau University
Almaty University of Power Engineering and Telecommunications after Gumarbek Daukeev
Lublin Technical University
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