A New Hybrid Approach Based on Discrete Wavelet Transform and Deep Learning for Traffic Sign Recognition in Autonomous Vehicles

The rapid advancement of autonomous vehicles has led to the widespread integration of advanced driver assistance systems, significantly improving vehicle control, safety, and compliance with traffic regulations. A crucial aspect of these systems is the reliable detection and recognition of traffic signs, which play a key role in managing urban traffic flow and ensuring road safety. However, traffic sign recognition remains a challenging task due to varying lighting conditions, occlusions, and diverse sign appearances. This paper presents a novel hybrid approach for efficient traffic sign recognition tailored to the needs of autonomous driving. The proposed method combines the Discrete Wavelet Transform for robust feature extraction with the powerful classification capabilities of Convolutional Neural Networks within a Deep Learning framework. The DWT effectively captures essential image characteristics while reducing noise and irrelevant details, providing a compact yet informative feature set for the CNN classifier. Extensive experiments were conducted to evaluate the performance of the system in real-world conditions. The proposed approach achieved an impressive recognition precision of 98%, demonstrating its ability to interpret and respond to traffic signs with high reliability. The results confirm the method’s robustness, real-time efficiency, and suitability for deployment in intelligent transportation systems and autonomous vehicles. Overall, this study highlights the complementary strengths of DWT and CNN within the broader context of Deep Learning, offering a significant improvement over conventional traffic sign recognition techniques. The proposed system represents a promising step toward enhancing the perception capabilities of autonomous vehicles, contributing to safer and more reliable navigation in complex traffic environments.