YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11
Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed...
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MDPI AG
2025-04-01
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| author | Makara Mao Min Hong |
| author_facet | Makara Mao Min Hong |
| author_sort | Makara Mao |
| collection | DOAJ |
| description | Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review systematically examines the evolution of the You Only Look Once (YOLO) object detection framework from YOLO-v1 to YOLO-v11, emphasizing architectural advancements such as attention-based feature refinement and Transformer integration and their impact on fabric defect detection. Unlike prior studies focusing on specific YOLO variants, this work comprehensively compares the entire YOLO family, highlighting key innovations and their practical implications. We also discuss the challenges, including dataset limitations, domain generalization, and computational constraints, proposing future solutions such as synthetic data generation, federated learning, and edge AI deployment. By bridging the gap between academic advancements and industrial applications, this review is a practical guide for selecting and optimizing YOLO models for fabric inspection, paving the way for intelligent quality control systems. |
| format | Article |
| id | doaj-art-a2d66b2bf59e4d249ce920b321a20861 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-a2d66b2bf59e4d249ce920b321a208612025-08-20T02:09:21ZengMDPI AGSensors1424-82202025-04-01257227010.3390/s25072270YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11Makara Mao0Min Hong1Department of Software Convergence, Soonchunhyang University, Asan-si 31538, Republic of KoreaDepartment of Computer Software Engineering, Soonchunhyang University, Asan-si 31538, Republic of KoreaAutomated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review systematically examines the evolution of the You Only Look Once (YOLO) object detection framework from YOLO-v1 to YOLO-v11, emphasizing architectural advancements such as attention-based feature refinement and Transformer integration and their impact on fabric defect detection. Unlike prior studies focusing on specific YOLO variants, this work comprehensively compares the entire YOLO family, highlighting key innovations and their practical implications. We also discuss the challenges, including dataset limitations, domain generalization, and computational constraints, proposing future solutions such as synthetic data generation, federated learning, and edge AI deployment. By bridging the gap between academic advancements and industrial applications, this review is a practical guide for selecting and optimizing YOLO models for fabric inspection, paving the way for intelligent quality control systems.https://www.mdpi.com/1424-8220/25/7/2270YOLO variantsreal-time defect detectionfabric detectiondeep learning in textilesconvolutional neural networkstextile industry |
| spellingShingle | Makara Mao Min Hong YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11 Sensors YOLO variants real-time defect detection fabric detection deep learning in textiles convolutional neural networks textile industry |
| title | YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11 |
| title_full | YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11 |
| title_fullStr | YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11 |
| title_full_unstemmed | YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11 |
| title_short | YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11 |
| title_sort | yolo object detection for real time fabric defect inspection in the textile industry a review of yolov1 to yolov11 |
| topic | YOLO variants real-time defect detection fabric detection deep learning in textiles convolutional neural networks textile industry |
| url | https://www.mdpi.com/1424-8220/25/7/2270 |
| work_keys_str_mv | AT makaramao yoloobjectdetectionforrealtimefabricdefectinspectioninthetextileindustryareviewofyolov1toyolov11 AT minhong yoloobjectdetectionforrealtimefabricdefectinspectioninthetextileindustryareviewofyolov1toyolov11 |