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However, traditional methods of defect detection bring problems of low detection accuracy and dependence on subjective judgment. In this study, the surface defects of steel strips are detected by a classic convolutional neural network method that is improved by the use of a transfer learning model. This model has the advantages of shorter training time, faster convergence, and more accurate weight parameters. The transfer learning model obtained through experiments secures better results in defect detection than the classic convolutional neural network method, as its accuracy of training and testing has reached about 98%. Finally, a model based on a full convolutional neural network (FCN) is proposed for segmenting the defective areas of steel strips.<\/jats:p>","DOI":"10.1155\/2021\/6637252","type":"journal-article","created":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T19:51:31Z","timestamp":1622317891000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Hot\u2010Rolled Steel Strip Surface Inspection Based on Transfer Learning Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4537-2898","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8337-4785","authenticated-orcid":false,"given":"Quanquan","family":"Lv","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"key":"e_1_2_9_1_2","first-page":"320","article-title":"Research on an online detection and recognition algorithm for surface defects of strip steel","volume":"26","author":"Yingli H.","year":"2015","journal-title":"Photoelectron Laser"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2019.06.020"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2019.01.011"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.3788\/OPE.20162410.2572"},{"key":"e_1_2_9_5_2","first-page":"1","article-title":"A survey of surface defect detection methods based on deep learning","volume":"46","author":"Tao X.","year":"2020","journal-title":"Acta Automatica Sinica"},{"key":"e_1_2_9_6_2","first-page":"106","article-title":"Surface quality detection of cold rolled strip based on BP neural network","volume":"6","author":"Wang C. 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