The algorithm for foggy weather target detection based on YOLOv5 in complex scenes

With the rapid development of urbanization and global climate warming, complex environments and adverse weather conditions pose significant challenges to the accuracy of object detection and driving safety in autonomous driving systems. Addressing the challenges of severe occlusion and noise interference in target detection within complex foggy scenes and considering the YOLOv5 model’s small size and fast processing capabilities, which meet the real-time processing demands in complex environments, it is particularly suited for resource-constrained vehicular systems. Consequently, this paper introduces the YOLOv5-RCBiW model tailored for vehicular vision perception aimed at enhancing feature extraction and recognition. Initially, the Receptive Field Block (RFB) is integrated with the Coordinate Attention (CA) mechanism to form the RFCA module, which emphasizes the importance of different features and optimizes receptive field spatial features. Furthermore, the Re-BiFPN module is constructed to enhance feature perception accuracy through bidirectional cross-scale connections and feature fusion, while the detection head at the P5 layer is replaced to improve recognition capabilities. Finally, a gradient gain loss function is introduced to reduce feature information loss and prevent model performance degradation, ensuring robustness and accuracy in complex environments. The comparative experimental results on the RTTS and Foggy Driving datasets indicate that the YOLOv5-RCBiW model significantly outperforms existing models in object detection accuracy under foggy and complex scenes. Additionally, in-vehicle experiments validate the model’s effectiveness and real-time performance in challenging environments.