A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling
In order to solve the problem of high computational resource consumption in fabric anomaly detection, we propose a lightweight network, GH-YOLOx, which integrates ghost convolutions and hierarchical GHNetV2 backbone together to capture both local and global anomaly features. At the same time, other...
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2038 |
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| author | Shuqin Cui Weihong Liu Min Li |
| author_facet | Shuqin Cui Weihong Liu Min Li |
| author_sort | Shuqin Cui |
| collection | DOAJ |
| description | In order to solve the problem of high computational resource consumption in fabric anomaly detection, we propose a lightweight network, GH-YOLOx, which integrates ghost convolutions and hierarchical GHNetV2 backbone together to capture both local and global anomaly features. At the same time, other innovative components, such as GhostConv, dynamic convolutions, feature fusion modules, and a shared group convolution head, are applied to effectively handle multi-scale issues. Lamp pruning accelerates inference, while channel-wise knowledge distillation enhances the pruned model’s accuracy. Experiments on fabric datasets demonstrate that GH-YOLOx can effectively reduce the number of parameters while achieving a higher detection rate than other lightweight models. Overall, our solution offers a practical approach to real-time fabric anomaly detection on mobile and embedded devices. |
| format | Article |
| id | doaj-art-eb6608a75b0147c890ba99e2906f31dd |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-eb6608a75b0147c890ba99e2906f31dd2025-08-20T03:03:25ZengMDPI AGSensors1424-82202025-03-01257203810.3390/s25072038A Lightweight Approach to Comprehensive Fabric Anomaly Detection ModelingShuqin Cui0Weihong Liu1Min Li2School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430072, ChinaSchool of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430072, ChinaSchool of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430072, ChinaIn order to solve the problem of high computational resource consumption in fabric anomaly detection, we propose a lightweight network, GH-YOLOx, which integrates ghost convolutions and hierarchical GHNetV2 backbone together to capture both local and global anomaly features. At the same time, other innovative components, such as GhostConv, dynamic convolutions, feature fusion modules, and a shared group convolution head, are applied to effectively handle multi-scale issues. Lamp pruning accelerates inference, while channel-wise knowledge distillation enhances the pruned model’s accuracy. Experiments on fabric datasets demonstrate that GH-YOLOx can effectively reduce the number of parameters while achieving a higher detection rate than other lightweight models. Overall, our solution offers a practical approach to real-time fabric anomaly detection on mobile and embedded devices.https://www.mdpi.com/1424-8220/25/7/2038lightweight networkfabric anomaly detectionlamp pruning techniqueknowledge distillation |
| spellingShingle | Shuqin Cui Weihong Liu Min Li A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling Sensors lightweight network fabric anomaly detection lamp pruning technique knowledge distillation |
| title | A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling |
| title_full | A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling |
| title_fullStr | A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling |
| title_full_unstemmed | A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling |
| title_short | A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling |
| title_sort | lightweight approach to comprehensive fabric anomaly detection modeling |
| topic | lightweight network fabric anomaly detection lamp pruning technique knowledge distillation |
| url | https://www.mdpi.com/1424-8220/25/7/2038 |
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