{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T12:22:03Z","timestamp":1789042923675,"version":"build-2803163510"},"reference-count":38,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Textile Research Journal"],"abstract":"<jats:p>Quality assurance in garment manufacturing remains a critical challenge for developing-country exporters, particularly in Ethiopia's emerging apparel sector where manual visual inspection predominates. This subjective, fatigue-prone process leads to inconsistent defect detection and limits production throughput. This study presents an automated garment defect recognition system integrating a VGGNet-19 convolutional neural network (CNN) for feature extraction with an artificial neural network (ANN) for multiclass classification. A dataset of 1000 trouser images encompassing four defect categories (broken stitching, open seams, seam slippage, and untrimmed threads) and defect-free samples was collected from Addis Ababa garment manufacturing facilities under factory lighting conditions. The proposed architecture achieved an overall classification accuracy of 86.7% on the held-out test set, with per-class performance of 86.7% for defect-free, 83.3% for broken stitching, 90.0% for open seams, 80.0% for seam slippage, and 96.7% for untrimmed threads. The system processes images in 0.45 seconds per sample on consumer-grade hardware, representing a substantial 60-fold improvement over manual inspection throughput while maintaining consistent defect detection criteria. Comparative analysis revealed that RGB color channels outperform grayscale conversion (86.7% vs. 80.0% accuracy), and the deep VGGNet-19 architecture exceeds the shallower AlexNet by 6.2 percentage points despite longer training requirements (6.2 hours vs. 3.5 hours). This work establishes a foundation for real-time, automated quality control in resource-constrained apparel manufacturing environments, with a return on investment estimated at 12\u201318 months for medium-sized Ethiopian factories.<\/jats:p>","DOI":"10.1177\/00405175261484722","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T06:32:41Z","timestamp":1788762761000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of an automatic garment defect recognition system using a machine learning approach"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4549-6145","authenticated-orcid":false,"given":"Alene","family":"Eyasu","sequence":"first","affiliation":[{"name":"Bahir Dar University, Ethiopian Institute of Textile and Fashion Technology, Department of Textile and Apparel Merchandising, P.O.Box 79, Bahir Dar, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9939-936X","authenticated-orcid":false,"given":"Asefaw","family":"Alene","sequence":"additional","affiliation":[{"name":"Bahir Dar University, Ethiopian Institute of Textile and Fashion Technology, Department of Computer Science, P.O.Box 79, Bahir Dar, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,6]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"The State of Fashion 2024","author":"McKinsey & Company","year":"2024","unstructured":"McKinsey & Company. 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