Defect intelligent recognition of membrane product based on deep learning
Defect detection plays a crucial role in the manufacturing industry, ensuring the quality of industrial products. Despite advancements in this field, current defect detection methods face two primary challenges: (1) extracting visually similar features from the background poses difficult and (2) the...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-08-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940241268952 |
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| _version_ | 1850097223388364800 |
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| author | Maonian Wu Ling Li Wei Peng Tao Wu Jinwei Yu Bo Zheng Shaojun Zhu |
| author_facet | Maonian Wu Ling Li Wei Peng Tao Wu Jinwei Yu Bo Zheng Shaojun Zhu |
| author_sort | Maonian Wu |
| collection | DOAJ |
| description | Defect detection plays a crucial role in the manufacturing industry, ensuring the quality of industrial products. Despite advancements in this field, current defect detection methods face two primary challenges: (1) extracting visually similar features from the background poses difficult and (2) these methods struggle to identify tiny defects in the target objects. We present a feature enhancement module called the SE-CAR, which aims to handle the identified problems effectively. This module is designed to efficiently capture tiny defects in images, prioritize defect information features, and ultimately enhance the model’s predictive performance and accuracy in defect recognition tasks. In addition, Distance-IoU Non-Maximum Suppression is employed as a substitution for the original Non-Maximum Suppression. This enhances the recognition accuracy of bounding boxes, ensuring that the model maintains high detection accuracy even after complex scenarios. Moreover, the proposed methodology exhibits broad applicability across a wide spectrum of prevalent defect detection paradigms. In empirical experiments, we employ the YOLOv7 architecture as the foundation framework, integrating the proposed methodologies for the purpose of detecting defects in the membrane. The empirical evidence demonstrates a notable improvement in the performance of detecting defects in membrane products using the SE-CAR feature enhancement module and Distance-IoU Non-Maximum Suppression algorithm. In comparison to baseline networks, an increase in mAp by 6.29%, precision by 1.63%, and recall by 8.76%. |
| format | Article |
| id | doaj-art-2b24c0b73b5a4ffaaf81e2c852b42563 |
| institution | DOAJ |
| issn | 0020-2940 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj-art-2b24c0b73b5a4ffaaf81e2c852b425632025-08-20T02:41:02ZengSAGE PublishingMeasurement + Control0020-29402025-08-015810.1177/00202940241268952Defect intelligent recognition of membrane product based on deep learningMaonian Wu0Ling Li1Wei Peng2Tao Wu3Jinwei Yu4Bo Zheng5Shaojun Zhu6Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, ChinaZhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, ChinaCollege of Computer Science and Technology, Guizhou University, Guiyang, ChinaHuzhou Institute, Collaborative Innovation Center for Membrane Separation and Water Treatment of Zhejiang Province, Zhejiang University of Technology, Huzhou, ChinaHuzhou Sennuo Fluorine Material Technology Co., Ltd, Huzhou, ChinaZhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, ChinaZhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, ChinaDefect detection plays a crucial role in the manufacturing industry, ensuring the quality of industrial products. Despite advancements in this field, current defect detection methods face two primary challenges: (1) extracting visually similar features from the background poses difficult and (2) these methods struggle to identify tiny defects in the target objects. We present a feature enhancement module called the SE-CAR, which aims to handle the identified problems effectively. This module is designed to efficiently capture tiny defects in images, prioritize defect information features, and ultimately enhance the model’s predictive performance and accuracy in defect recognition tasks. In addition, Distance-IoU Non-Maximum Suppression is employed as a substitution for the original Non-Maximum Suppression. This enhances the recognition accuracy of bounding boxes, ensuring that the model maintains high detection accuracy even after complex scenarios. Moreover, the proposed methodology exhibits broad applicability across a wide spectrum of prevalent defect detection paradigms. In empirical experiments, we employ the YOLOv7 architecture as the foundation framework, integrating the proposed methodologies for the purpose of detecting defects in the membrane. The empirical evidence demonstrates a notable improvement in the performance of detecting defects in membrane products using the SE-CAR feature enhancement module and Distance-IoU Non-Maximum Suppression algorithm. In comparison to baseline networks, an increase in mAp by 6.29%, precision by 1.63%, and recall by 8.76%.https://doi.org/10.1177/00202940241268952 |
| spellingShingle | Maonian Wu Ling Li Wei Peng Tao Wu Jinwei Yu Bo Zheng Shaojun Zhu Defect intelligent recognition of membrane product based on deep learning Measurement + Control |
| title | Defect intelligent recognition of membrane product based on deep learning |
| title_full | Defect intelligent recognition of membrane product based on deep learning |
| title_fullStr | Defect intelligent recognition of membrane product based on deep learning |
| title_full_unstemmed | Defect intelligent recognition of membrane product based on deep learning |
| title_short | Defect intelligent recognition of membrane product based on deep learning |
| title_sort | defect intelligent recognition of membrane product based on deep learning |
| url | https://doi.org/10.1177/00202940241268952 |
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