Research on Designing a Fabric Surface Defect Detection System Using YOLO in Vietnam

Research on Designing a Fabric Surface Defect Detection System Using YOLO in Vietnam
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© 2025 by IJETT Journal
Volume-73 Issue-10
Year of Publication : 2025
Author : Huong Dinh Mai, Nam Pham Van, Huong Pham Thi Quynh, Hung Pham Van
DOI : 10.14445/22315381/IJETT-V73I10P101

How to Cite?
Huong Dinh Mai, Nam Pham Van, Huong Pham Thi Quynh, Hung Pham Van,"Research on Designing a Fabric Surface Defect Detection System Using YOLO in Vietnam", International Journal of Engineering Trends and Technology, vol. 73, no. 10, pp.1-9, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I10P101

Abstract
In the textile industry, detecting and classifying fabric defects is essential for maintaining product quality, improving production efficiency, and reducing manufacturing costs. This study presents a defect detection system based on YOLOv11, trained with a dataset that includes common defects such as yarn defects and stains. The system was tested on an experimental setup designed to match real production conditions. Results show that, at a fabric speed of 3 m/min, the model achieved over 95% accuracy for stain defects and up to 90% accuracy for yarn defects, meeting real-time inspection requirements. The system reduces dependence on manual inspection, lowers error rates, and improves reliability in fabric quality control. The results show that deep learning and Industry 4.0 work well for textile inspection and could also be applied in other industries.

Keywords
Image processing, YOLO, Machine Learning, Fabric surface defects, Textile industry.

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