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In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R\u2010CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang\u2010Taiyuan high\u2010speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.<\/jats:p>","DOI":"10.1155\/2021\/2565500","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T20:36:53Z","timestamp":1627936613000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3612-3793","authenticated-orcid":false,"given":"Danyang","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4196-5875","authenticated-orcid":false,"given":"Liming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2327-4245","authenticated-orcid":false,"given":"Shubin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2243-2301","authenticated-orcid":false,"given":"Xiaodong","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6359-4091","authenticated-orcid":false,"given":"Shuguang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4015-8721","authenticated-orcid":false,"given":"Qianqian","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9050-0441","authenticated-orcid":false,"given":"Ji","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizheng","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,2]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"2312","article-title":"DeepRail: automatic visual detection system for railway surface defect using bayesian CNN and attention network","volume":"45","author":"Jin X.","year":"2019","journal-title":"Acta Automatica Sinica"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/jsen.2020.2977366"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2018.05.060"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2020.3006324"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2020.3039301"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1784\/insi.2021.63.4.199"},{"key":"e_1_2_10_7_2","doi-asserted-by":"crossref","unstructured":"XiaY. 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