{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T06:51:56Z","timestamp":1782370316191,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fujian Science and Technology Guidance Project","award":["2024H0039"],"award-info":[{"award-number":["2024H0039"]}]},{"name":"Fuzhou Major Science and Technology \u201cChallenge-Based\u201d Project","award":["2024-ZD-008"],"award-info":[{"award-number":["2024-ZD-008"]}]},{"name":"Fujian Provincial Social Science Fund Project","award":["FJ2025C213"],"award-info":[{"award-number":["FJ2025C213"]}]},{"name":"Minjiang University Science and Technology Project","award":["MJY21022"],"award-info":[{"award-number":["MJY21022"]}]},{"award":["MJY21022"],"award-info":[{"award-number":["MJY21022"]}],"id":[{"id":"https:\/\/ror.org\/00s7tkw17","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Conventional defect inspection for warp-knitted lace relies on manual work and negative-sample-based training, resulting in low efficiency, frequent false detections and poor adaptability. This study presents a novel AI visual inspection system centered on positive-sample learning, which is built upon a five-layer 5G + Industrial Internet distributed architecture. Supported by modified looms, high-precision imaging devices and an optimized YOLOv5s model, the system accomplishes intelligent defect detection. A positive-sample self-learning paradigm and dual-model collaboration mechanism are proposed to reduce the demand for negative samples and cut labeling expenses. The integration of CBAM, FPN + PAN structure, self-supervised learning and hybrid loss further strengthens the recognition performance for subtle defects under complex patterns. Industrial tests show that the system reaches a grid-level classification accuracy of 95% and a frame-level detection rate over 98%, with a detection speed of 30 m\/min. It reduces labor costs and product reject rates by 40% and 30% correspondingly while running stably in real production. This method breaks the constraints of traditional training modes, provides a scalable intelligent solution for the digital upgrading of the warp-knitted lace industry, and promotes the high-quality development of textile manufacturing.<\/jats:p>","DOI":"10.3390\/info17070623","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T05:48:16Z","timestamp":1782366496000},"page":"623","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and Application of an AI Visual Defect Detection System for Warp-Knitted Lace Based on 5G+ Technology"],"prefix":"10.3390","volume":"17","author":[{"given":"Taohai","family":"Yan","sequence":"first","affiliation":[{"name":"Fujian Key Laboratory of Functional Textile Fibers and Products, Clothing and Design Faculty, Minjiang University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongze","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Materials, University of Manchester, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajing","family":"Shi","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Functional Textile Fibers and Products, Clothing and Design Faculty, Minjiang University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaowang","family":"Lin","sequence":"additional","affiliation":[{"name":"Fujian Donglong Knitting & Textile Co., Ltd., Fuzhou 350217, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Ji","sequence":"additional","affiliation":[{"name":"Guobin Yaqi Textile Co., Ltd., Yinchuan 751400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2775","DOI":"10.1007\/s43615-025-00555-x","article-title":"Digitalisation and Green Strategies: A Systematic Review of the Textile, Apparel and Fashion Industries","volume":"5","author":"Orisadare","year":"2025","journal-title":"Circ. 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