{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:15:49Z","timestamp":1782314149068,"version":"3.54.5"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,2]],"date-time":"2018-06-02T00:00:00Z","timestamp":1527897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["E2017210113"],"award-info":[{"award-number":["E2017210113"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Educational Commission Foundation of Hebei Province","award":["ZD2017070"],"award-info":[{"award-number":["ZD2017070"]}]},{"DOI":"10.13039\/501100012166","name":"National key research and development program","doi-asserted-by":"publisher","award":["2016YFB1200401-107"],"award-info":[{"award-number":["2016YFB1200401-107"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M621100"],"award-info":[{"award-number":["2017M621100"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.<\/jats:p>","DOI":"10.3390\/s18061796","type":"journal-article","created":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T08:52:03Z","timestamp":1528102323000},"page":"1796","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model"],"prefix":"10.3390","volume":"18","author":[{"given":"Baoxian","family":"Wang","sequence":"first","affiliation":[{"name":"Structure Health Monitoring and Control Institute, Shijiazhuang Tiedao University, Shijiazhuang 050043, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weigang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Structure Health Monitoring and Control Institute, Shijiazhuang Tiedao University, Shijiazhuang 050043, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1061\/(ASCE)0887-3801(2003)17:4(255)","article-title":"Analysis of edge detection techniques for crack identification in bridges","volume":"17","author":"Abdelqader","year":"2003","journal-title":"J. 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