FogGuard: guarding YOLO against fog using perceptual loss
Soheil Gharatappeh, Sepideh Neshatfar, Salimeh Yasaei Sekeh, Vikas Dhiman

TL;DR
FogGuard is a fog-aware object detection network that enhances YOLO's performance in foggy conditions using a novel perceptual loss, significantly improving detection accuracy without increasing inference time.
Contribution
We introduce FogGuard, which incorporates a Teacher-Student Perceptual loss to improve YOLO's robustness in foggy environments, a novel approach compared to existing enhancement and domain adaptation methods.
Findings
Achieves 69.43% mAP on RTTS, outperforming YOLOv3's 57.78%.
Improves detection robustness in foggy conditions with minimal inference overhead.
Demonstrates effectiveness across standard datasets like PASCAL VOC and RTTS.
Abstract
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 include image enhancement techniques like IA-YOLO and domain adaptation methods. While image enhancement aims to generate clear images from foggy ones, which is more challenging than object detection in foggy images, domain adaptation does not require labeled data in the target domain. Our approach involves fine-tuning on a specific dataset to address these challenges efficiently. FogGuard compensates for foggy conditions in the scene, ensuring robust performance by incorporating YOLOv3 as the baseline algorithm and introducing a…
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Taxonomy
TopicsImage Enhancement Techniques · Fire Detection and Safety Systems
MethodsBNB Customer Service Number +1-833-534-1729 · Average Pooling · Residual Connection · Softmax · Batch Normalization · Global Average Pooling · Convolution · 1x1 Convolution · Logistic Regression · k-Means Clustering
