{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:29:44Z","timestamp":1770294584002,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects.<\/jats:p>","DOI":"10.3390\/s24175636","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T07:45:47Z","timestamp":1725003947000},"page":"5636","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhihao","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Electronic lnformation and Electrical Engineering, Yangtze University, Jingzhou 434100, China"}]},{"given":"Xiaohan","family":"Dou","sequence":"additional","affiliation":[{"name":"School of Electronic lnformation and Electrical Engineering, Yangtze University, Jingzhou 434100, China"}]},{"given":"Xiaolong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic lnformation and Electrical Engineering, Yangtze University, Jingzhou 434100, China"}]},{"given":"Chengqi","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Electronic lnformation and Electrical Engineering, Yangtze University, Jingzhou 434100, China"}]},{"given":"Anqi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic lnformation and Electrical Engineering, Yangtze University, Jingzhou 434100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6175-4100","authenticated-orcid":false,"given":"Gengpei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic lnformation and Electrical Engineering, Yangtze University, Jingzhou 434100, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1057\/s11369-016-0008-y","article-title":"The US Aerospace Industry: A Manufacturing Powerhouse","volume":"51","author":"Soshkin","year":"2016","journal-title":"Bus. 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