Textile Defect Detection Algorithm Based on the Improved YOLOv8
Automatic detection of textile defects is a crucial factor in improving textile quality. Fast and accurate detection of these defects is key to achieving automation in the textile industry. However, the detection of textile defects faces challenges such as small defect targets, low contrast between...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10838510/ |
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| author | Wenfei Song Du Lang Jiahui Zhang Meilian Zheng Xiaoming Li |
| author_facet | Wenfei Song Du Lang Jiahui Zhang Meilian Zheng Xiaoming Li |
| author_sort | Wenfei Song |
| collection | DOAJ |
| description | Automatic detection of textile defects is a crucial factor in improving textile quality. Fast and accurate detection of these defects is key to achieving automation in the textile industry. However, the detection of textile defects faces challenges such as small defect targets, low contrast between defects and the background, and significant variations in the aspect ratio of defects. To address these issues, this study proposes a new method for textile defect detection based on an improved version of You Only Look Once Version 8(YOLOv8) called DA-YOLOv8s. Deep & Cross Network(DCNv2) is introduced into the Backbone Network to replace the C2F module, enhancing the extraction of network features; an self-attention mechanism, Polarized Self-Attention(PSA), is adopted to increase feature fusion capability and reduce feature loss in both channel and spatial dimensions; finally, a Small Object Detection Head (SOHead) is added to improve the feature extraction ability for small targets. Experimental results show that the improved YOLOv8 algorithm achieves has achieved mAP@0.5 and mAP of 44.6% and 48.6% respectively, which is an improvement of 4.2% and 3.8% over the original algorithm, and also outperforms the Optimal YOLOv9s model and the latest YOLOv11s model in these two metrics. The speed of textile defect detection has reached 257.38 frames per second (FPS) and the floating-point operation speed is 36.6 GFLOPS, ensuring the accuracy and speed of textile defect detection, with practical engineering application value. |
| format | Article |
| id | doaj-art-b319c1c4643d41eb9a1c213e6c3115c2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b319c1c4643d41eb9a1c213e6c3115c22025-01-24T00:01:34ZengIEEEIEEE Access2169-35362025-01-0113112171123110.1109/ACCESS.2025.352877110838510Textile Defect Detection Algorithm Based on the Improved YOLOv8Wenfei Song0https://orcid.org/0000-0001-7520-0638Du Lang1https://orcid.org/0009-0004-0569-0933Jiahui Zhang2https://orcid.org/0009-0004-2772-054XMeilian Zheng3Xiaoming Li4https://orcid.org/0000-0002-9956-1793School of Information and Design, Zhejiang Industry Polytechnic College, Shaoxing, Zhejiang, ChinaSchool of Information and Design, Zhejiang Industry Polytechnic College, Shaoxing, Zhejiang, ChinaSchool of Information and Design, Zhejiang Industry Polytechnic College, Shaoxing, Zhejiang, ChinaSchool of Management, Zhejiang University of Technology, Hangzhou, Zhejiang, ChinaSchool of International Business, Zhejiang Yuexiu University, Shaoxing, Zhejiang, ChinaAutomatic detection of textile defects is a crucial factor in improving textile quality. Fast and accurate detection of these defects is key to achieving automation in the textile industry. However, the detection of textile defects faces challenges such as small defect targets, low contrast between defects and the background, and significant variations in the aspect ratio of defects. To address these issues, this study proposes a new method for textile defect detection based on an improved version of You Only Look Once Version 8(YOLOv8) called DA-YOLOv8s. Deep & Cross Network(DCNv2) is introduced into the Backbone Network to replace the C2F module, enhancing the extraction of network features; an self-attention mechanism, Polarized Self-Attention(PSA), is adopted to increase feature fusion capability and reduce feature loss in both channel and spatial dimensions; finally, a Small Object Detection Head (SOHead) is added to improve the feature extraction ability for small targets. Experimental results show that the improved YOLOv8 algorithm achieves has achieved mAP@0.5 and mAP of 44.6% and 48.6% respectively, which is an improvement of 4.2% and 3.8% over the original algorithm, and also outperforms the Optimal YOLOv9s model and the latest YOLOv11s model in these two metrics. The speed of textile defect detection has reached 257.38 frames per second (FPS) and the floating-point operation speed is 36.6 GFLOPS, ensuring the accuracy and speed of textile defect detection, with practical engineering application value.https://ieeexplore.ieee.org/document/10838510/Interest point detectiontextile industryquality managementYOLOv8textile defect detectionpolarized self-attention |
| spellingShingle | Wenfei Song Du Lang Jiahui Zhang Meilian Zheng Xiaoming Li Textile Defect Detection Algorithm Based on the Improved YOLOv8 IEEE Access Interest point detection textile industry quality management YOLOv8 textile defect detection polarized self-attention |
| title | Textile Defect Detection Algorithm Based on the Improved YOLOv8 |
| title_full | Textile Defect Detection Algorithm Based on the Improved YOLOv8 |
| title_fullStr | Textile Defect Detection Algorithm Based on the Improved YOLOv8 |
| title_full_unstemmed | Textile Defect Detection Algorithm Based on the Improved YOLOv8 |
| title_short | Textile Defect Detection Algorithm Based on the Improved YOLOv8 |
| title_sort | textile defect detection algorithm based on the improved yolov8 |
| topic | Interest point detection textile industry quality management YOLOv8 textile defect detection polarized self-attention |
| url | https://ieeexplore.ieee.org/document/10838510/ |
| work_keys_str_mv | AT wenfeisong textiledefectdetectionalgorithmbasedontheimprovedyolov8 AT dulang textiledefectdetectionalgorithmbasedontheimprovedyolov8 AT jiahuizhang textiledefectdetectionalgorithmbasedontheimprovedyolov8 AT meilianzheng textiledefectdetectionalgorithmbasedontheimprovedyolov8 AT xiaomingli textiledefectdetectionalgorithmbasedontheimprovedyolov8 |