Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 Framework
Fabric defect detection is a critical task in the textile industry, requiring high precision and recall to ensure effective quality control. This study presents an enhanced YOLOv8-based framework that integrates novel attention mechanisms and advanced architectural modules to improve detection accur...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11007006/ |
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| _version_ | 1849470948077469696 |
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| author | Yonghua Mao Guowen Wang Yingcang Ma Xiaolin Gui |
| author_facet | Yonghua Mao Guowen Wang Yingcang Ma Xiaolin Gui |
| author_sort | Yonghua Mao |
| collection | DOAJ |
| description | Fabric defect detection is a critical task in the textile industry, requiring high precision and recall to ensure effective quality control. This study presents an enhanced YOLOv8-based framework that integrates novel attention mechanisms and advanced architectural modules to improve detection accuracy and robustness. The framework incorporates the SimAM attention mechanism within the SPPF module and adopts an optimized Dilation-wise Residual (DWR) structure in the backbone. Comprehensive ablation studies and comparisons with state-of-the-art methods validate the effectiveness of the proposed approach. The enhanced model achieves a mAP50-95 of 74.3%, outperforming the baseline by 4.7 percentage points, with marked improvements in detecting challenging defect categories. While the framework demonstrates significant advancements, limitations in dataset diversity and computational efficiency are acknowledged. Future work will focus on resource optimization, dataset augmentation, and extending the framework’s applicability to other domains. |
| format | Article |
| id | doaj-art-d4af8caf525f44cfbcbbffbe960c0ba1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d4af8caf525f44cfbcbbffbe960c0ba12025-08-20T03:24:59ZengIEEEIEEE Access2169-35362025-01-0113967679678110.1109/ACCESS.2025.357045511007006Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 FrameworkYonghua Mao0https://orcid.org/0000-0002-7450-408XGuowen Wang1https://orcid.org/0009-0004-7506-5201Yingcang Ma2https://orcid.org/0000-0002-3356-7535Xiaolin Gui3https://orcid.org/0000-0003-4384-9891School of Computer Science/School of Science, Xi’an Polytechnic University, Xi’an, ChinaSchool of Computer Science/School of Science, Xi’an Polytechnic University, Xi’an, ChinaSchool of Computer Science/School of Science, Xi’an Polytechnic University, Xi’an, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaFabric defect detection is a critical task in the textile industry, requiring high precision and recall to ensure effective quality control. This study presents an enhanced YOLOv8-based framework that integrates novel attention mechanisms and advanced architectural modules to improve detection accuracy and robustness. The framework incorporates the SimAM attention mechanism within the SPPF module and adopts an optimized Dilation-wise Residual (DWR) structure in the backbone. Comprehensive ablation studies and comparisons with state-of-the-art methods validate the effectiveness of the proposed approach. The enhanced model achieves a mAP50-95 of 74.3%, outperforming the baseline by 4.7 percentage points, with marked improvements in detecting challenging defect categories. While the framework demonstrates significant advancements, limitations in dataset diversity and computational efficiency are acknowledged. Future work will focus on resource optimization, dataset augmentation, and extending the framework’s applicability to other domains.https://ieeexplore.ieee.org/document/11007006/Fabric defect detectionYOLOv8small target detectionattention mechanismC2f-R2DWRSPPF-AEMSim |
| spellingShingle | Yonghua Mao Guowen Wang Yingcang Ma Xiaolin Gui Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 Framework IEEE Access Fabric defect detection YOLOv8 small target detection attention mechanism C2f-R2DWR SPPF-AEMSim |
| title | Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 Framework |
| title_full | Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 Framework |
| title_fullStr | Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 Framework |
| title_full_unstemmed | Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 Framework |
| title_short | Enhancing Fabric Defect Detection With Attention Mechanisms and Optimized YOLOv8 Framework |
| title_sort | enhancing fabric defect detection with attention mechanisms and optimized yolov8 framework |
| topic | Fabric defect detection YOLOv8 small target detection attention mechanism C2f-R2DWR SPPF-AEMSim |
| url | https://ieeexplore.ieee.org/document/11007006/ |
| work_keys_str_mv | AT yonghuamao enhancingfabricdefectdetectionwithattentionmechanismsandoptimizedyolov8framework AT guowenwang enhancingfabricdefectdetectionwithattentionmechanismsandoptimizedyolov8framework AT yingcangma enhancingfabricdefectdetectionwithattentionmechanismsandoptimizedyolov8framework AT xiaolingui enhancingfabricdefectdetectionwithattentionmechanismsandoptimizedyolov8framework |