FogGuard: guarding YOLO against fog using perceptual loss

arXiv:2403.08939v1 Announce Type: cross
Abstract: In this paper, we present a novel fog-aware object detection network called FogGuard, 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 fall into two main categories, 1) image enhancement such as IA-YOLO 2) domain adaptation based approaches. Image enhancement based techniques attempt to generate fog-free image. However, …

abstract algorithms arxiv autonomous autonomous driving autonomous driving systems challenges cs.cv cs.lg detection driving impact loss network networks neural networks novel object paper reliability systems type weather yolo