TRAFFIC SIGN DETECTION UNDER ADVERSE CONDITIONS

Abstract

Automatic traffic sign detection and recognition play a crucial role in real-world applications such as autonomous driving systems and driver assistance technologies. This project aims to develop a system using deep learning techniques to enhance road safety and assist autonomous vehicles by automatically detecting road signs and signals in adverse situations such as intense lighting, snowfall, and rainy conditions. In this project, we utilize a Convolutional Neural Network (CNN) algorithm to train the model using a large dataset provided by the German Traffic Sign Recognition Benchmark (GTSRB). Additionally, OpenCV is employed for image processing, while TensorFlow/Keras is used for model training and image classification. The proposed model achieves an accuracy of 95.33% on the test dataset, demonstrating its robustness in detecting traffic signs under challenging conditions such as intense lighting, foggy weather, or heavy snowfall. This system can be integrated into driver assistance systems and autonomous vehicle. In the future, it can be further improved for real-time traffic sign detection and enhanced to achieve higher accuracy in complex environment