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Internet Technol."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>With the remarkable technological development in cyber-physical systems, industry 4.0 has evolved by use of a significant concept named digital twin (DT). However, it is still difficult to construct a relationship between twin simulation and a real scenario considering dynamic variations, especially when dealing with small surface defect detection tasks with high performance and computation resource requirements. In this article, we aim to construct cyber-manufacturing systems to achieve a DT solution for small surface defect detection task. Focusing on DT-based solution, the proposed system consists of an Edge\u2013Cloud architecture and a surface defect detection algorithm. Considering dynamic characteristics and real-time response requirement, Edge\u2013Cloud architecture is built to achieve smart manufacturing by efficiently collecting, processing, analyzing, and storing data produced by factory. A deep learning\u2013based algorithm is then constructed to detect surface defeats based on multi-modal data, i.e., imaging and depth data. Experiments show the proposed algorithm could achieve high accuracy and recall in small defeat detection task, thus constructing DT in cyber-manufacturing.<\/jats:p>","DOI":"10.1145\/3571734","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T15:11:45Z","timestamp":1668697905000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":57,"title":["Digital Twin of Intelligent Small Surface Defect Detection with Cyber-manufacturing Systems"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3022-3718","authenticated-orcid":false,"given":"Yirui","family":"Wu","sequence":"first","affiliation":[{"name":"College of Computer and Information, Hohai University China, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2892-6588","authenticated-orcid":false,"given":"Hao","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7779-4158","authenticated-orcid":false,"given":"Guoqiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Novel Software Technology, Nanjing University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7051-5347","authenticated-orcid":false,"given":"Tong","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Novel Software Technology, Nanjing University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3462777"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41558-021-00986-y"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.593"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2022.03.010"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.236"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.691"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01498"},{"key":"e_1_3_1_9_2","article-title":"Two path gland segmentation algorithm of colon pathological image based on local semantic guidance","author":"Ding Songtao","year":"2022","unstructured":"Songtao Ding, Hongyu Wang, Hu Lu, Michele Nappi, and Shaohua Wan. 2022. 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