In autonomous driving systems, fog decreases visibility and contrast, blurs the boundary of objects, and increases scale variations, which results in missed detections. To solve these problems, we introduce Fog-YOLO, a lightweight real-time detector based on the YOLOv12 framework. First, we design the A2C2f-FSA module, which enhances the representation of fog-affected areas and low-contrast objects by modeling long-range dependencies in the frequency domain. This module effectively suppresses the interference of fog and background noise while maintaining low computational overhead. Second, we propose a bidirectional feature fusion module (BFFM) that uses decoupled attention paths to fuse deep semantic features and shallow texture details. This approach enhances robustness across multiple scales, ensuring the capture of fine-grained texture information and the preservation of global contextual information in foggy environments. Third, we introduce GSConv, which reduces parameters and computational cost by balancing spatial correlation modeling and computational complexity, optimizing the feature extraction process. Finally, we design the F-WIoU v3 loss function, which optimizes bounding box regression through dynamic focusing and difficulty re-weighting strategies, thereby reducing the influence of low-quality samples while improving the model’s localization robustness in foggy conditions. Experiments on the RTTS real-world fog dataset and the VOC-FOG synthetic dataset show that Fog-YOLO outperforms the baseline by 5.2% and 7.3% in mAP@0.5 with real-time inference speed. Overall, Fog-YOLO outperforms mainstream lightweight detectors, demonstrating its practical usefulness for autonomous driving in foggy environments.
Wang et al. (Tue,) studied this question.