Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM
In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjus...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9382260/ |
| _version_ | 1818602343795523584 |
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| author | Changlu Xu Linsheng Li Jiwei Li Chuanbo Wen |
| author_facet | Changlu Xu Linsheng Li Jiwei Li Chuanbo Wen |
| author_sort | Changlu Xu |
| collection | DOAJ |
| description | In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect image to weaken the influence of non-defective areas and enhance the defect features. Then, Canny algorithm and the AND logical operation were used to extract the image of defect area. Next, the texture feature, edge feature, and HOG feature were combined to extract the feature of the defect area image. Finally, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was used to automatically identify and classify defect images. The experimental results show that the proposed method in this paper can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic defect detection and identification method. |
| first_indexed | 2024-12-16T13:05:47Z |
| format | Article |
| id | doaj.art-66238c8f1e654165a1fa51c769eadb08 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:05:47Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-66238c8f1e654165a1fa51c769eadb082022-12-21T22:30:45ZengIEEEIEEE Access2169-35362021-01-019852328523910.1109/ACCESS.2021.30676419382260Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVMChanglu Xu0https://orcid.org/0000-0001-8410-3493Linsheng Li1https://orcid.org/0000-0002-8032-0906Jiwei Li2https://orcid.org/0000-0002-0681-7738Chuanbo Wen3https://orcid.org/0000-0003-2391-8888School of Electrical Engineering, Shanghai Dianji University, Shanghai, ChinaSchool of Electrical Engineering, Shanghai Dianji University, Shanghai, ChinaSchool of Electrical Engineering, Shanghai Dianji University, Shanghai, ChinaSchool of Electrical Engineering, Shanghai Dianji University, Shanghai, ChinaIn order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect image to weaken the influence of non-defective areas and enhance the defect features. Then, Canny algorithm and the AND logical operation were used to extract the image of defect area. Next, the texture feature, edge feature, and HOG feature were combined to extract the feature of the defect area image. Finally, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was used to automatically identify and classify defect images. The experimental results show that the proposed method in this paper can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic defect detection and identification method.https://ieeexplore.ieee.org/document/9382260/Machine visionsurface defects of lithium battery pole piecedefect detectionsupport vector machinedefect segmentation |
| spellingShingle | Changlu Xu Linsheng Li Jiwei Li Chuanbo Wen Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM IEEE Access Machine vision surface defects of lithium battery pole piece defect detection support vector machine defect segmentation |
| title | Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM |
| title_full | Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM |
| title_fullStr | Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM |
| title_full_unstemmed | Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM |
| title_short | Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-Feature Fusion and PSO-SVM |
| title_sort | surface defects detection and identification of lithium battery pole piece based on multi feature fusion and pso svm |
| topic | Machine vision surface defects of lithium battery pole piece defect detection support vector machine defect segmentation |
| url | https://ieeexplore.ieee.org/document/9382260/ |
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