文献类型：期刊文献

英文题名：Pedestrian and vehicle detection in foggy weather using dehazeformer

作者：Gao, Jiadan[1];Dai, Lei[1];Zhang, Wei[2];Hu, Qingchun[1];Chen, Guangyong[2];Chen, Zhihua[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]China Natl Aeronaut Radio Elect Res Inst, Shanghai 200241, Peoples R China

年份：2025

卷号：41

期号：11

起止页码：8523

外文期刊名：VISUAL COMPUTER

收录：;EI(收录号：20251618262640);WOS:【SCI-EXPANDED(收录号:WOS:001469002800001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 62272164 and No. 62306113) and the Aeronautical Science Foundation of China (Grant No. 202400550S7003 and No. 202400550S7004);

语种：英文

外文关键词：Deep learning; Target detection; Image dehazing; Autonomous driving

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.