FogGuard: guarding YOLO against fog using perceptual loss
Soheil Gharatappeh Sepideh Neshatfar Salimeh Yasaei Sekeh Vikas Dhiman
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, retrieving a fogless
image from a foggy image is a much harder problem than detecting objects in a
foggy image. Domain-adaptation based approaches, on the other hand, do not make
use of labelled datasets in the target domain. Both categories of approaches
are attempting to solve a harder version of the problem. Our approach builds
over fine-tuning on the
  Our framework is specifically designed to compensate for foggy conditions
present in the scene, ensuring robust performance even. We adopt YOLOv3 as the
baseline object detection algorithm and introduce a novel Teacher-Student
Perceptual loss, to high accuracy object detection in foggy images.
  Through extensive evaluations on common datasets such as PASCAL VOC and RTTS,
we demonstrate the improvement in performance achieved by our network. We
demonstrate that FogGuard achieves 69.43\% mAP, as compared to 57.78\% for
YOLOv3 on the RTTS dataset.
  Furthermore, we show that while our training method increases time
complexity, it does not introduce any additional overhead during inference
compared to the regular YOLO network.