Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny
The current advanced neural network models are expanding in size and complexity to achieve improved detection accuracy. This study designs a lightweight fabric defect detection algorithm based on YOLOv7-tiny, called YOLOv7-tiny-MGCK. Its objectives are to improve the performance of fabric defect det...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Journal of Natural Fibers |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15440478.2024.2352753 |
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| _version_ | 1850164301278478336 |
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| author | Tang Li Mei Shunqi Shi Yishan Zhou Shi Zheng Quan Hongkai Jiang Xu Qiao Zhang Zhiming |
| author_facet | Tang Li Mei Shunqi Shi Yishan Zhou Shi Zheng Quan Hongkai Jiang Xu Qiao Zhang Zhiming |
| author_sort | Tang Li |
| collection | DOAJ |
| description | The current advanced neural network models are expanding in size and complexity to achieve improved detection accuracy. This study designs a lightweight fabric defect detection algorithm based on YOLOv7-tiny, called YOLOv7-tiny-MGCK. Its objectives are to improve the performance of fabric defect detection against complex backgrounds and to find a balance between the algorithm’s lightweight nature and its accuracy. The algorithm utilizes the Mish activation function, known for its superior nonlinear performance capability and smoother curve, enabling the neural network to manage more complex challenges. The Ghost convolution module is also incorporated to reduce computation and model parameters. The lightweight upsampling technique CARAFE facilitates the flexible extraction of deep features, coupled with their integration with shallow features. In addition, an improved K-Means clustering algorithm, KMMP, is employed to select appropriate anchor box for fabric defects. The experimental results show: a reduction in the number of parameters by 45.5% and computational volume by 41.0%, along with increases in precision by 3.9%, recall by 7.0%, and mAP by 3.0%. These results indicated that the improved algorithm achieves a more effective balance between detection performance and the requirement for a lightweight solution. |
| format | Article |
| id | doaj-art-a4cd021ce1274015bb3affcbe29ef138 |
| institution | OA Journals |
| issn | 1544-0478 1544-046X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj-art-a4cd021ce1274015bb3affcbe29ef1382025-08-20T02:22:02ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2024-12-0121110.1080/15440478.2024.2352753Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-TinyTang Li0Mei Shunqi1Shi Yishan2Zhou Shi3Zheng Quan4Hongkai Jiang5Xu Qiao6Zhang Zhiming7Hubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, ChinaHubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, ChinaHubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, ChinaHubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, ChinaHubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, ChinaThe Chinese University of Hongkong, Shenzhen, ChinaHubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, ChinaHubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, ChinaThe current advanced neural network models are expanding in size and complexity to achieve improved detection accuracy. This study designs a lightweight fabric defect detection algorithm based on YOLOv7-tiny, called YOLOv7-tiny-MGCK. Its objectives are to improve the performance of fabric defect detection against complex backgrounds and to find a balance between the algorithm’s lightweight nature and its accuracy. The algorithm utilizes the Mish activation function, known for its superior nonlinear performance capability and smoother curve, enabling the neural network to manage more complex challenges. The Ghost convolution module is also incorporated to reduce computation and model parameters. The lightweight upsampling technique CARAFE facilitates the flexible extraction of deep features, coupled with their integration with shallow features. In addition, an improved K-Means clustering algorithm, KMMP, is employed to select appropriate anchor box for fabric defects. The experimental results show: a reduction in the number of parameters by 45.5% and computational volume by 41.0%, along with increases in precision by 3.9%, recall by 7.0%, and mAP by 3.0%. These results indicated that the improved algorithm achieves a more effective balance between detection performance and the requirement for a lightweight solution.https://www.tandfonline.com/doi/10.1080/15440478.2024.2352753Neural networkfabric defect detectionactivation functionGhost moduleupsamplingclustering algorithm |
| spellingShingle | Tang Li Mei Shunqi Shi Yishan Zhou Shi Zheng Quan Hongkai Jiang Xu Qiao Zhang Zhiming Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny Journal of Natural Fibers Neural network fabric defect detection activation function Ghost module upsampling clustering algorithm |
| title | Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny |
| title_full | Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny |
| title_fullStr | Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny |
| title_full_unstemmed | Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny |
| title_short | Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny |
| title_sort | research on fabric defect detection algorithm based on lightweight yolov7 tiny |
| topic | Neural network fabric defect detection activation function Ghost module upsampling clustering algorithm |
| url | https://www.tandfonline.com/doi/10.1080/15440478.2024.2352753 |
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