{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T14:24:31Z","timestamp":1785335071035,"version":"3.55.0"},"reference-count":14,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,8,10]]},"abstract":"<jats:p>Aiming at the inconsistency of manual detection of mobile phone screen defects, the image feature extraction of traditional machine learning is often set based on experience, resulting in unsatisfactory detection results. Therefore, a mobile phone screen defect detection model (Ghostbackbone) which is proposed by this paper based on YOLOv5\u200as and Ghostbottleneck. The bottleneck of Ghostbackbone mainly uses and improves the Ghostbottleneck of GhostNet. The attention module of Ghostbackbone uses Coordinated Attention and Depthwise Separable Convolution for parameter reduction. Finally, Ghostbackbone uses YOLOv5 as the object detector to train the mobile phone screen defect dataset. The experimental results show that the parameter quantity of Ghostbackbone is 24% of that of YOLOv5\u200as, the average time of detecting a single picture is only 2% lower than that of YOLOv5\u200as, and the mAP0.5\u200a:\u200a0.95 is 2% higher than that of MobilenetV3\u200as.<\/jats:p>","DOI":"10.3233\/jifs-212896","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T11:28:05Z","timestamp":1651577285000},"page":"4335-4349","source":"Crossref","is-referenced-by-count":10,"title":["Visual defects detection model of mobile phone screen"],"prefix":"10.1177","volume":"43","author":[{"given":"Ge","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Multimedia Technology, Research Center for Intelligent Engineering and Educational Application, Beijing Normal University at Zhuhai, China"},{"name":"Engineering Lab on Intelligent Perception for Internet of Things (ELIP), Shenzhen Graduate School, Peking University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijian","family":"Lai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Multimedia Technology, Research Center for Intelligent Engineering and Educational Application, Beijing Normal University at Zhuhai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Multimedia Technology, Research Center for Intelligent Engineering and Educational Application, Beijing Normal University at Zhuhai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-212896_ref1","doi-asserted-by":"crossref","unstructured":"Zhao, Zhixuan , et al. A surface defect detection method based on positive samples, Pacific Rim International Conference on Artificial Intelligence, Springer, Cham, 2018.","DOI":"10.1007\/978-3-319-97310-4_54"},{"issue":"4","key":"10.3233\/JIFS-212896_ref2","first-page":"900","article-title":"Glass surface defect detection methon based on multiscale convolution neural network","volume":"26","author":"Xiong Honglin","year":"2020","journal-title":"Computer Integrated Manufacturing Systems bf"},{"issue":"5","key":"10.3233\/JIFS-212896_ref3","first-page":"115","article-title":"Image Crack Detection with Fully Convolutional Network Based on Deep Learning","volume":"30","author":"Wang Sen","year":"2018","journal-title":"Journal of Computer-Aided Design & Computer Graphics bf"},{"issue":"4","key":"10.3233\/JIFS-212896_ref4","first-page":"629","article-title":"Survey on deep learning object detection","volume":"25","author":"Zhao Yongqiang","year":"2020","journal-title":"Journal of Image and Graphics bf"},{"key":"10.3233\/JIFS-212896_ref8","doi-asserted-by":"crossref","unstructured":"Liu, Wei , et al., Ssd: Single shot multibox detector, European conference on computer vision, Springer, Cham, 2016.","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"2","key":"10.3233\/JIFS-212896_ref13","first-page":"84","article-title":"Pavement filling crack detection method based on improved Faster CNN","volume":"48","author":"Sun Zhaoyun","year":"2020","journal-title":"Journal of South China University of Technology (Natural Science Edition) bf"},{"issue":"3","key":"10.3233\/JIFS-212896_ref14","doi-asserted-by":"crossref","first-page":"218","DOI":"10.2472\/jsms.69.218","article-title":"A Two-Step Screening System for Surface Crack Using Object Detection and Recognition Technique Based on Deep Learning","volume":"69","author":"Kazuki Shigemura","year":"2020","journal-title":"Journal of Society of Materials Science, Japan bf"},{"key":"10.3233\/JIFS-212896_ref16","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.asoc.2016.10.030","article-title":"Automatic surface defect detection for mobile phone screen glass based on machine vision","volume":"52","author":"Jian, Chuanxia","year":"2017","journal-title":"Applied Soft Computing"},{"issue":"9","key":"10.3233\/JIFS-212896_ref25","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.3390\/rs13091619","article-title":"A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5","volume":"13","author":"Yan, Bin","year":"2021","journal-title":"Remote Sensing"},{"issue":"9","key":"10.3233\/JIFS-212896_ref26","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He, Kaiming","year":"2015","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"10.3233\/JIFS-212896_ref28","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s10845-019-01476-x","article-title":"Segmentation-based deep-learning approach for surface-defect detection","volume":"31","author":"Taik, Domen","year":"2020","journal-title":"Journal of Intelligent Manufacturing"},{"key":"10.3233\/JIFS-212896_ref29","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1993\/1\/012035"},{"key":"10.3233\/JIFS-212896_ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCBDA51879.2021.9442557"},{"issue":"7","key":"10.3233\/JIFS-212896_ref32","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.imavis.2011.02.002","article-title":"Automated fabric defect detection\u2014a review","volume":"29","author":"Ngan","year":"2011","journal-title":"Image Vis Comput"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-212896","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:46:41Z","timestamp":1777456001000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-212896"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,10]]},"references-count":14,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/jifs-212896","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,10]]}}}