In this paper, we present FogGuard, a novel fog-aware object detection network designed to address the challenges posed by foggy weather conditions. Autonomous driving systems heavily rely on accurate object detection algorithms, but adverse weather conditions can significantly impact the reliability of deep neural networks (DNNs). Existing approaches, such as image enhancement techniques (like IA-YOLO) and domain adaptation methods, aim to tackle a more complex issue by either removing artifacts related to weather conditions or narrowing the distribution gap between the source and target domains through data augmentation. However, these efforts result in a notable rise in time complexity for the original algorithm. FogGuard compensates for foggy conditions in the scene efficiently, ensuring robust performance by incorporating YOLOv3 as the baseline algorithm and introducing a unique Teacher-Student Perceptual loss for accurate object detection in foggy environments. Through comprehensive evaluations on standard datasets like PASCAL VOC and RTTS, our network significantly improves performance, achieving a 69.43% mAP compared to YOLOv3’s 57.78% on the RTTS dataset. Additionally, we demonstrate that while our training method slightly increases time complexity, it doesn’t add overhead during inference compared to the regular YOLO network. The code for our work is available at https://github.com/Sekeh-Lab/FogGuard