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However, imperfections like protruding and loop fibers, known as \u2018hairiness\u2019, can significantly impact yarn quality, leading to defects in the final fabrics. Controlling yarn quality in the spinning process is essential, but current commercial equipment is expensive and limited to analyzing only a few parameters. The advent of artificial intelligence (AI) offers a promising solution to this challenge. By utilizing deep learning algorithms, a model can detect various yarn irregularities, including thick places, thin places, and neps, while characterizing hairiness by distinguishing between loop and protruding fibers in digital yarn images. This paper proposes a novel approach using deep learning, specifically, an enhanced algorithm based on YOLOv5s6, to characterize different types of yarn hairiness. Key performance indicators include precision, recall, F1-score, mAP0.5:0.95, and mAP0.5. The experimental results show significant improvements, with the proposed algorithm increasing model mAP0.5 by 5% to 6% and mAP0.5:0.95 by 11% to 12% compared to the standard YOLOv5s6 model. A 10k-fold cross-validation method is applied, providing an accurate estimate of the performance on unseen data and facilitating unbiased comparisons with other approaches.<\/jats:p>","DOI":"10.3390\/app15010149","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T07:00:37Z","timestamp":1735628437000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7809-5554","authenticated-orcid":false,"given":"Filipe","family":"Pereira","sequence":"first","affiliation":[{"name":"MEtRICs Research Center, University of Minho, Campus of Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"},{"name":"Algoritmi Research Centre, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal"},{"name":"2Ai, School of Technology, IPCA, 4750-810 Barcelos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3315-1134","authenticated-orcid":false,"given":"Helena","family":"Lopes","sequence":"additional","affiliation":[{"name":"MEtRICs Research Center, University of Minho, Campus of Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6977-7737","authenticated-orcid":false,"given":"Leandro","family":"Pinto","sequence":"additional","affiliation":[{"name":"2Ai, School of Technology, IPCA, 4750-810 Barcelos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4438-6713","authenticated-orcid":false,"given":"Filomena","family":"Soares","sequence":"additional","affiliation":[{"name":"Algoritmi Research Centre, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1434-1060","authenticated-orcid":false,"given":"Rosa","family":"Vasconcelos","sequence":"additional","affiliation":[{"name":"2C2T Research Centre, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4917-2474","authenticated-orcid":false,"given":"Jos\u00e9","family":"Machado","sequence":"additional","affiliation":[{"name":"MEtRICs Research Center, University of Minho, Campus of Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4658-5844","authenticated-orcid":false,"given":"V\u00edtor","family":"Carvalho","sequence":"additional","affiliation":[{"name":"Algoritmi Research Centre, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal"},{"name":"2Ai, School of Technology, IPCA, 4750-810 Barcelos, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,27]]},"reference":[{"key":"ref_1","unstructured":"Ara\u00fajo, M., and Melo, E.M.C. 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