{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T20:16:34Z","timestamp":1648930594060},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>The detection of yarn hairiness is essential in the production of chemical fiber yarn packages, and it is difficult to detect them because of their small features, which are easy to be missed and confused with another non-defective feature broken ends. To detect defects accurately and efficiently in the appearance of yarn packages, a CenterNet defect detection algorithm (CenterNet-CBAM) combining with attention mechanism is proposed. Two types of confusing target images, \u201cyarn hairiness\u201d and \u201cbroken ends\u201d, are collected, and an object detection model based on CenterNet-CBAM is constructed, and the Recall of CenterNet-CBAM in the two categories of \u201cyarn hairiness\u201d and \u201cbroken ends\u201d is 90.20% and 85.42%, Precision is 93.88% and 93.48%, AP is 90.91% and 90.93%, and MAP is 90.92% for the two categories, respectively, which were better than CenterNet and YOLOv4, which verified the effectiveness of the experimental method.<\/jats:p>","DOI":"10.3233\/faia220028","type":"book-chapter","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T13:35:14Z","timestamp":1646228114000},"source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Detection Method of Yarn Hairiness Using CenterNet-CBAM"],"prefix":"10.3233","author":[{"given":"Jialu","family":"Tang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Donghua University, Shanghai 201620, China"}]},{"given":"Zhongliang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Donghua University, Shanghai 201620, China"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Donghua University, Shanghai 201620, China"}]},{"given":"Xingli","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Donghua University, Shanghai 201620, China"}]},{"given":"Song","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Materials, University of Manchester, Manchester M13 9PL, United Kingdom"}]},{"given":"Qingqi","family":"Dong","sequence":"additional","affiliation":[{"name":"Zhejiang Shuangtu New Material Co., Ltd. Hangzhou 311200, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Design Studies and Intelligence Engineering"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T13:35:15Z","timestamp":1646228115000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220028","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,28]]}}}