{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:02:24Z","timestamp":1787022144128,"version":"build-2736575974"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T00:00:00Z","timestamp":1712707200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The automatic detection of the degree of surface corrosion on metal structures is of significant importance for assessing structural damage and safety. To effectively identify the corrosion status on the surface of coastal metal facilities, this study proposed a CBG-YOLOv5s model for metal surface corrosion detection, based on the YOLOv5s model. Firstly, we integrated the Convolutional Block Attention Module (CBAM) into the C3 module and developed the C3CBAM module. This module effectively enhanced the channel and spatial attention capabilities of the feature map, thereby improving the feature representation. Second, we introduced a multi-scale feature fusion concept in the feature fusion part of the model and added a small target detection layer to improve small target detection. Finally, we designed a lighter C3Ghost module, which reduced the number of parameters and the computational load of the model, thereby improving the running speed of the model. In addition, to verify the effectiveness of our method, we constructed a dataset containing 6000 typical images of metal surface corrosion and conducted extensive experiments on this dataset. The results showed that compared to the YOLOv5s model and several other commonly used object detection models, our method achieved superior performance in terms of detection accuracy and speed.<\/jats:p>","DOI":"10.1371\/journal.pone.0300440","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T13:34:50Z","timestamp":1712756090000},"page":"e0300440","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Detection and recognition of metal surface corrosion based on CBG-YOLOv5s"],"prefix":"10.1371","volume":"19","author":[{"given":"Mingjiao","family":"Fu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhitong","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingzhi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5503-1336","authenticated-orcid":true,"given":"Zhendong","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"key":"pone.0300440.ref001","doi-asserted-by":"crossref","first-page":"104708","DOI":"10.1016\/j.marpol.2021.104708","article-title":"Impact of sci-tech finance on the innovation efficiency of China\u2019s marine industry[J].","volume":"133","author":"U Sheng X","year":"2021","journal-title":"Marine Policy"},{"key":"pone.0300440.ref002","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.marpol.2018.10.019","article-title":"The role of the marine industry in China\u2019s national economy: An input\u2013output analysis[J].","volume":"99","author":"Y Wang","year":"2019","journal-title":"Marine Policy"},{"issue":"1","key":"pone.0300440.ref003","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1038\/s41529-017-0005-2","article-title":"The cost of corrosion in China[J].","volume":"1","author":"B Hou","year":"2017","journal-title":"npj Materials Degradation"},{"key":"pone.0300440.ref004","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T. 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