{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:34:04Z","timestamp":1780414444110,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971405"],"award-info":[{"award-number":["41971405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671442"],"award-info":[{"award-number":["41671442"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271449"],"award-info":[{"award-number":["42271449"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crack detection is essential for the safety maintenance of road infrastructure. However, there are two major limitations to detecting road cracks accurately: (1) tiny cracks usually possess less distinctive features and are more susceptible to noises, so they are apt to be ignored; (2) most existing methods extract cracks with coarse and thicker boundaries, which needs further improvement. To address the above limitations, we propose CTCD-Net: a Cross-layer Transmission network for tiny road Crack Detection. Firstly, we propose a cross-layer information transmission module based on an attention mechanism to compensate for the disadvantage of unobvious features of tiny cracks. With this module, the feature information from upper layers is transmitted to the next one, layer by layer, to achieve information enhancement and emphasize the feature representation of tiny crack regions. Secondly, we design a boundary refinement block to further improve the accuracy of crack boundary locations, which refines boundaries by learning the residuals between the label images and the interim coarse maps. Extensive experiments conducted on three crack datasets demonstrate the superiority and effectiveness of the proposed CTCD-Net. In particular, our method largely improves the accuracy and completeness of tiny crack detection.<\/jats:p>","DOI":"10.3390\/rs15082185","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T01:33:43Z","timestamp":1682040823000},"page":"2185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["CTCD-Net: A Cross-Layer Transmission Network for Tiny Road Crack Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8621-5018","authenticated-orcid":false,"given":"Chong","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"Beidou Research Institute, Faculty of Engineering, South China Normal University, Foshan 528000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3523-8994","authenticated-orcid":false,"given":"Luliang","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Chu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaokui","family":"Li","sequence":"additional","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"ref_1","first-page":"102836","article-title":"SD-GCN: Saliency-based dilated graph convolution network for pavement crack extraction from 3D point clouds","volume":"111","author":"Ma","year":"2022","journal-title":"Int. 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