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<p>Recently, the application of target detection in urban transportation has become increasingly widespread. However, in foggy environments, due to low visibility, targets, such as pedestrians and vehicles, are not obvious, which easily lead to low detection accuracy and poor robustness. We investigate the detection methods in foggy environments, using the DehazeFormer and YOLOv8 as benchmark algorithms. To address the issue of ambiguous target features in foggy weather, we introduce an innovative spatial pyramid pooling structure, SimSPPFCSPC, to accelerate network convergence and boost the accuracy and efficiency of the target detection. To address the issue of detail loss and contextual information loss in foggy targets, we suggest substituting the backbone feature extraction network with EfficientViT network, implementing a lightweight multi-scale linear attention mechanism to augment the model’s resilience and enhance its detection capability. To address the issue of target size variation in foggy weather, we propose an IoU-based dynamic adjustment gradient distribution strategy to refine the loss function, bolstering the model’s generalization capability. Experimental results indicate the proposed method not only surpasses the baseline in performance, but also occupies an advantageous position among similar target detection methods, demonstrating excellent detection performance. Specifically, our method achieves Precision, Recall, mAP of 62.7%, 37.0% and 42.1% on Foggy Cityscapes, respectively. In addition, it attains Precision, Recall, mAP of 81.8%, 74.8% and 82.2% on PASCAL VOC, respectively. The related code of our method is available at <a href="https://github.com/chuanchuan0423/YOLO-SEW">https://github.com/chuanchuan0423/YOLO-SEW</a>.</p>

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Pedestrian and vehicle detection in foggy weather using dehazeformer

  • 期刊论文

  • 发表日期：

    2025年4月17日

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  • Jiadan Gao,
  • Lei Dai,
  • Wei Zhang,
  • Qingchun Hu,
  • Guangyong Chen,
  • Zhihua Chen

摘要

Recently, the application of target detection in urban transportation has become increasingly widespread. However, in foggy environments, due to low visibility, targets, such as pedestrians and vehicles, are not obvious, which easily lead to low detection accuracy and poor robustness. We investigate the detection methods in foggy environments, using the DehazeFormer and YOLOv8 as benchmark algorithms. To address the issue of ambiguous target features in foggy weather, we introduce an innovative spatial pyramid pooling structure, SimSPPFCSPC, to accelerate network convergence and boost the accuracy and efficiency of the target detection. To address the issue of detail loss and contextual information loss in foggy targets, we suggest substituting the backbone feature extraction network with EfficientViT network, implementing a lightweight multi-scale linear attention mechanism to augment the model’s resilience and enhance its detection capability. To address the issue of target size variation in foggy weather, we propose an IoU-based dynamic adjustment gradient distribution strategy to refine the loss function, bolstering the model’s generalization capability. Experimental results indicate the proposed method not only surpasses the baseline in performance, but also occupies an advantageous position among similar target detection methods, demonstrating excellent detection performance. Specifically, our method achieves Precision, Recall, mAP of 62.7%, 37.0% and 42.1% on Foggy Cityscapes, respectively. In addition, it attains Precision, Recall, mAP of 81.8%, 74.8% and 82.2% on PASCAL VOC, respectively. The related code of our method is available at https://github.com/chuanchuan0423/YOLO-SEW.