{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:45:13Z","timestamp":1777290313964,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,3]],"date-time":"2023-06-03T00:00:00Z","timestamp":1685750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Academician Mao Ming Workstation","award":["XS-JSFW-KCZNJS-202303-001"],"award-info":[{"award-number":["XS-JSFW-KCZNJS-202303-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To address this issue, this study proposes a foggy weather detection method based on the YOLOv5s framework, named YOLOv5s-Fog. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer called SwinFocus. Additionally, the decoupled head is incorporated into the model, and the conventional non-maximum suppression method is replaced with Soft-NMS. The experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model, YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.<\/jats:p>","DOI":"10.3390\/s23115321","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:57:47Z","timestamp":1685933867000},"page":"5321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios"],"prefix":"10.3390","volume":"23","author":[{"given":"Xianglin","family":"Meng","sequence":"first","affiliation":[{"name":"School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"National Industrial Innovation Center of Intelligent Equipment, Changzhou 213300, China"}]},{"given":"Lili","family":"Fan","sequence":"additional","affiliation":[{"name":"National Industrial Innovation Center of Intelligent Equipment, Changzhou 213300, China"}]},{"given":"Jingjing","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China"},{"name":"National Industrial Innovation Center of Intelligent Equipment, Changzhou 213300, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bijelic, M., Gruber, T., Mannan, F., Kraus, F., Ritter, W., Dietmayer, K., and Heide, F. 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