FogGuard is a new object detection network designed to address challenges posed by foggy weather conditions. It utilizes a Teacher-Student Perceptual loss to enhance accuracy in detecting objects in foggy images. The method incorporates synthetic fog generation and fine-tuning on clear and foggy datasets to improve performance. Extensive evaluations demonstrate the superiority of FogGuard over existing methods, achieving higher mean Average Precision (mAP) on datasets like PASCAL VOC and RTTS. The approach ensures robust performance even in adverse weather conditions, without introducing overhead during inference.
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