Deep Learning Based Traffic Sign Detection for Smart Transport Systems

Traffic sign detection and recognition (TSDR) are vital for autonomous driving and advanced driver-assistance systems (ADAS), ensuring road safety and efficient navigation. This project develops an effective TSDR system using Convolutional Neural Networks (CNNs) for their superior image classification capabilities. The system is trained on a dataset of traffic sign images, incorporating preprocessing techniques like grayscale conversion, histogram equalization, and data normalization to enhance image quality. Data augmentation further improves model generalization and reduces overfitting risks.The CNN architecture extracts spatial features using convolutional and pooling layers, followed by fully connected layers for classification. It outperforms Recurrent Neural Networks (RNNs) and Gated Recurrent Units (GRUs) in accuracy, owing to its ability to recognize spatial hierarchies. Metrics such as accuracy and loss validate its effectiveness. Future work will focus on real-time implementation, optimization for embedded systems, and enhancing performance in diverse environments. CNNs prove robust for TSDR applications. Keywords- Convolutional Neural Network (CNN), Traffic Sign, Deep Learning (DL), Advanced Driver Assistance Systems.