Vehicle detection method in adverse weather conditions based on multitask deep learning

With the rapid development of autonomous driving technology and intelligent transportation systems, vehicle detection based on computer vision has become a key task in perception systems. However, adverse weather conditions in reality seriously affect the accuracy and robustness of vehicle detection. we propose a vehicle detection method in adverse weather based on multi-task deep learning. This method integrates image restoration and object detection tasks in the same network by designing an end-to-end joint training framework, and uses shared features and collaborative optimization to improve image quality and detection performance. In addition, we introduce a weather-aware attention mechanism to dynamically adjust the feature fusion method according to the weather type of the input image, thereby enhancing the adaptability of the model under different weather conditions. Experimental results show that compared with traditional methods and existing severe weather detection methods, this method has significantly improved detection accuracy and robustness, and has strong potential for practical application.