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Open AccessArticle
Efficient Fabric Classification and Object Detection Using YOLOv10
by
Makara Mao
Makara Mao 1,
Ahyoung Lee
Ahyoung Lee 2 and
Min Hong
Min Hong 3,*
1
Department of Software Convergence, Soonchunhyang University, Asan-si 31538, Republic of Korea
2
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
3
Department of Computer Software Engineering, Soonchunhyang University, Asan-si 31538, Republic of Korea
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(19), 3840; https://doi.org/10.3390/electronics13193840 (registering DOI)
Submission received: 29 August 2024
/
Revised: 24 September 2024
/
Accepted: 27 September 2024
/
Published: 28 September 2024
Abstract
The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It is highly relevant in industries like textiles, where speed and accuracy are critical. In the textile industry, accurate fabric type detection and classification are essential for improving quality control, optimizing inventory management, and enhancing customer satisfaction. This paper proposes a new approach using the YOLOv10 model, which offers enhanced detection accuracy, processing speed, and detection on the torn path of each type of fabric. We developed and utilized a specialized, annotated dataset featuring diverse textile samples, including cotton, hanbok, cotton yarn-dyed, and cotton blend plain fabrics, to detect the torn path in fabric. The YOLOv10 model was selected for its superior performance, leveraging advancements in deep learning architecture and applying data augmentation techniques to improve adaptability and generalization to the various textile patterns and textures. Through comprehensive experiments, we demonstrate the effectiveness of YOLOv10, which achieved an accuracy of 85.6% and outperformed previous YOLO variants in both precision and processing speed. Specifically, YOLOv10 showed a 2.4% improvement over YOLOv9, 1.8% over YOLOv8, 6.8% over YOLOv7, 5.6% over YOLOv6, and 6.2% over YOLOv5. These results underscore the significant potential of YOLOv10 in automating fabric detection processes, thereby enhancing operational efficiency and productivity in textile manufacturing and retail.
Share and Cite
MDPI and ACS Style
Mao, M.; Lee, A.; Hong, M.
Efficient Fabric Classification and Object Detection Using YOLOv10. Electronics 2024, 13, 3840.
https://doi.org/10.3390/electronics13193840
AMA Style
Mao M, Lee A, Hong M.
Efficient Fabric Classification and Object Detection Using YOLOv10. Electronics. 2024; 13(19):3840.
https://doi.org/10.3390/electronics13193840
Chicago/Turabian Style
Mao, Makara, Ahyoung Lee, and Min Hong.
2024. "Efficient Fabric Classification and Object Detection Using YOLOv10" Electronics 13, no. 19: 3840.
https://doi.org/10.3390/electronics13193840
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