Real Time Traffic Sign Detection Using Deep Neural Networks

This chapter explores the development and implementation of a real-time traffic sign detection system using deep neural networks, specifically Convolutional Neural Networks (CNNs). The project addresses critical challenges in traffic sign detection, such as varying illumination, localized recognition, and diverse real-world conditions. The proposed method utilizes the German Traffic Sign Benchmarks Dataset and an improved Mask R-CNN for enhanced detection and recognition. A TextToSpeech module is integrated to provide real-time voice alerts, particularly for speed limit signs. The system's architecture includes multiple convolutional layers, max-pooling layers, and tightly linked layers, with activation functions like 'tanh' and 'relu' to improve feature extraction and classification. The model achieved near-perfect accuracy, with training and validation metrics converging to above 99% by the sixth epoch. The confusion matrix and error analysis further confirm the model's high precision and recall rates. The trained model, 'traffic_sign_recognizer.h5,' is available for future applications, showcasing its potential in enhancing traffic safety, particularly in autonomous driving contexts.