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To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement.<\/jats:p>","DOI":"10.3390\/s23115138","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T15:29:52Z","timestamp":1685287792000},"page":"5138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Youhao","family":"Ni","sequence":"first","affiliation":[{"name":"Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxiao","family":"Mao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7125-0961","authenticated-orcid":false,"given":"Yuguang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1187-0824","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Zong","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 210096, China"},{"name":"Nanjing Highway Development (Group) Co., Ltd., Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.conbuildmat.2008.01.001","article-title":"Performance evaluation of a high durability asphalt binder and a high durability asphalt mixture for bridge deck pavements","volume":"23","author":"Park","year":"2009","journal-title":"Constr. 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