Traffic Sign Recognition for Advanced Driver Assistance Systems Using Deep Convolutional Neural Networks

Traffic accidents remain a significant global issue, with human error and driver fatigue being primary contributing factors. In particular, the failure to observe and interpret traffic signs in unfamiliar environments poses severe safety risks. This paper presents the development of a robust Traffic Sign Recognition (TSR) system designed for Advanced Driver Assistance Systems (ADAS). Utilizing Deep Learning techniques, specifically Convolutional Neural Networks (CNNs), the proposed model detects and classifies traffic signs from dashcam imagery with high precision. The system is trained and validated on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Preprocessing techniques, including histogram equalization and data augmentation, are employed to enhance model generalization across varying lighting conditions and geometric distortions. Experimental results demonstrate that the proposed architecture achieves a classification accuracy of 98.4%, outperforming traditional machine learning approaches. This study establishes a foundation for real-time driver alerts and autonomous vehicle navigation.