A target detection model HR-YOLO for advanced driver assistance systems in foggy conditions

To improve the accuracy and real-time performance of detection algorithms in Advanced Driver Assistance Systems (ADAS) under foggy conditions, this paper introduces HR-YOLO, an improved YOLO-based model specifically designed for vehicle and pedestrian detection. To enhance detection performance under complex meteorological conditions, several critical modules have been optimized. First, the Efficient High-Precision Defogging Network (EHPD-Net) is introduced to strengthen feature extraction. Inspired by the Global Attention Mechanism (GAM), the Enhanced Global-Spatial Attention (EGSA) module is incorporated to effectively improve the detection of small targets in foggy conditions. Second, the Depth-Normalized Defogging Network (DND-Net) is applied to enhance image quality. Additionally, the Dynamic Sample (DySample) module is integrated into the neck network, complemented by optimizations to the convolution and C2f modules, which significantly improve feature fusion efficiency. Furthermore, The Wise Intersection over Union (WIoU) loss function is introduced to enhance target localization accuracy. The robustness and accuracy of HR-YOLO were validated through experiments on two foggy weather datasets: RTTS and Foggy Cityscapes. The results indicate that HR-YOLO achieved a mean Average Precision (mAP) of 79.8% on the RTTS dataset, surpassing the baseline by 5.9%. On the Foggy Cityscapes dataset, it achieved an mAP of 49.5%, representing a 9.7% improvement over the baseline. This model serves as an effective solution for target detection tasks under foggy conditions and establishes a foundation for future advancements in this field.