AL-YOLO: Accurate and Lightweight Vehicle and Pedestrian Detector in Foggy Weather

Object detection remains a crucial challenge in computer vision, particularly under adverse weather conditions such as fog, where reduced visibility significantly impacts detection accuracy. Many existing methods, including image restoration and domain adaptation techniques, attempt to address these issues but often result in increased network parameters or negative transfer effects, limiting their practicality on resource-constrained devices. In this study we introduce AL-YOLO designed specifically for effective vehicle and pedestrian detection in foggy weather. By adapting YOLOv5s architecture, we integrate the MobileViTv2 with an Inverted Residual Block as the backbone, optimizing computational efficiency and enabling robust feature extraction. Additionally, we incorporate a C3RFE module to expand the receptive field, which significantly enhances detection performance in low-visibility conditions. Our experimental results on the Foggy-Cityscape dataset demonstrate that AL-YOLO achieved a 33% reduction in model parameters and a 2% mean Average Precision (mAP) improvement over YOLOv5s, emphasizing its efficiency and suitability for real-time fog-resistant object detection on resource-limited devices. Code and pretrained models are available at: https://github.com/BehdadSDP/AL-YOLO.