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.

随着城市化的快速发展和全球气候变暖，复杂的环境和恶劣的天气条件对自动驾驶系统的目标检测精度和驾驶安全提出了重大挑战。解决了复杂雾景下目标检测中严重遮挡和噪声干扰的挑战，并考虑到 YOLOv5 模型体积小、处理能力快，满足复杂环境下的实时处理需求，特别适用于资源受限的车载系统。因此，本文引入了为车辆视觉感知量身定制的 YOLOv5-RCBiW 模型，旨在增强特征提取和识别。最初，感受野阻滞 （RFB） 与坐标注意力 （CA） 机制集成形成 RFCA 模块，该模块强调不同特征的重要性并优化感受野空间特征。此外，构建了 Re-BiFPN 模块，通过双向交叉尺度连接和特征融合来提高特征感知精度，同时更换了 P5 层的检测头以提高识别能力。最后，引入梯度增益损失函数，以减少特征信息损失并防止模型性能下降，确保复杂环境下的鲁棒性和准确性。在 RTTS 和 Foggy Driving 数据集上的比较实验结果表明，YOLOv5-RCBiW 模型在雾和复杂场景下的目标检测精度明显优于现有模型。此外，车载实验验证了该模型在具有挑战性的环境中的有效性和实时性能。