{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T15:06:24Z","timestamp":1793977584168,"version":"4.8.0"},"reference-count":73,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\/MCTES","award":["UIDB\/05549:2Ai"],"award-info":[{"award-number":["UIDB\/05549:2Ai"]}]},{"name":"FCT\/MCTES","award":["UIDP\/05549:2Ai"],"award-info":[{"award-number":["UIDP\/05549:2Ai"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System\/Enterprise Resource Planning (MES\/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes.<\/jats:p>","DOI":"10.3390\/electronics14183692","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T09:32:32Z","timestamp":1758187952000},"page":"3692","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Textile Defect Detection Using Artificial Intelligence and Computer Vision\u2014A Preliminary Deep Learning Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5114-6522","authenticated-orcid":false,"given":"R\u00faben","family":"Machado","sequence":"first","affiliation":[{"name":"CeNTI\u2014Centre for Nanotechnology and Advanced Materials, 4760-034 Vila Nova de Famalic\u00e3o, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis A. M.","family":"Barros","sequence":"additional","affiliation":[{"name":"CeNTI\u2014Centre for Nanotechnology and Advanced Materials, 4760-034 Vila Nova de Famalic\u00e3o, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasco","family":"Vieira","sequence":"additional","affiliation":[{"name":"CeNTI\u2014Centre for Nanotechnology and Advanced Materials, 4760-034 Vila Nova de Famalic\u00e3o, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8296-8124","authenticated-orcid":false,"given":"Fl\u00e1vio Dias da","family":"Silva","sequence":"additional","affiliation":[{"name":"CeNTI\u2014Centre for Nanotechnology and Advanced Materials, 4760-034 Vila Nova de Famalic\u00e3o, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo","family":"Costa","sequence":"additional","affiliation":[{"name":"CeNTI\u2014Centre for Nanotechnology and Advanced Materials, 4760-034 Vila Nova de Famalic\u00e3o, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4658-5844","authenticated-orcid":false,"given":"Vitor","family":"Carvalho","sequence":"additional","affiliation":[{"name":"2Ai, School of Technology, Polytechnic University of C\u00e1vado and Ave, 4750-810 Barcelos, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"ref_1","unstructured":"(2025, July 10). 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