Research on Optimization and Application of Intelligent Vehicle Recognition Algorithms for Rainy Environments

Abstract

With the rapid development of intelligent transportation systems and autonomous driving technologies, vehicle recognition under adverse weather conditions has become a critical issue to be addressed. This study focuses on the optimization of intelligent vehicle recognition algorithms for rainy environments, proposing an improved vehicle detection model by integrating deep learning and multi-modal data fusion techniques. By introducing a spatial attention mechanism, an image restoration module, and a multi-sensor data fusion strategy, the algorithm's recognition accuracy and robustness under complex conditions such as low visibility and raindrop interference have been significantly enhanced. Experiments conducted on the DAWN dataset validated the effectiveness of the model, showing that the optimized algorithm achieves a mean average precision (mAP) of 89.7% in rainy environments, representing a 12.3% improvement over the traditional YOLOv5 model. This research provides theoretical support and technical solutions for the practical application of intelligent transportation systems under adverse weather conditions.