AUTOMATIC DETECTION AND RECOGNITION OF ROAD SIGNS USING CONVOLUTIONAL NEURAL NETWORKS

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.