YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8
The aluminum laminate pouch of pouch batteries is highly prone to deformation, which can cause various surface defects, thereby affecting their service life and potentially posing safety hazards. To address this problem, we propose an algorithm named YOLOv8-UCB for detecting surface defects in pouch...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807296/ |
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| author | Hao Hao Xiang Yu |
| author_facet | Hao Hao Xiang Yu |
| author_sort | Hao Hao |
| collection | DOAJ |
| description | The aluminum laminate pouch of pouch batteries is highly prone to deformation, which can cause various surface defects, thereby affecting their service life and potentially posing safety hazards. To address this problem, we propose an algorithm named YOLOv8-UCB for detecting surface defects in pouch batteries, which is based on the YOLOv8 model. First, while retaining the original network structure, we replaced the backbone of YOLOv8 with the UniRepLKNet feature extraction network to achieve a larger receptive field. Second, we constructed a distributed focal detection head CLLAHead, to better capture the features at different scales. Additionally, after each Concat layer in the head, we incorporated BiFormer attention mechanisms to enhance content-aware perception. Finally, we replaced the loss function with Slide Loss to address the issue of sample classification imbalance. The experimental results indicate that the improved algorithm achieved significant increases in precision, recall, mAP@0.5 and mAP@0.5:0.95, with improvements of 11.7%, 8.2%, 8.9% and 6.5% respectively, over the baseline model. In comparative studies against contemporaneous methodologies, the refined algorithm exhibited pronounced superiority in detection capabilities. |
| format | Article |
| id | doaj-art-8dac3c01f1cc4b0db548a7bfc1f41ed0 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8dac3c01f1cc4b0db548a7bfc1f41ed02025-08-20T02:56:47ZengIEEEIEEE Access2169-35362024-01-011219489919491010.1109/ACCESS.2024.352018110807296YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8Hao Hao0https://orcid.org/0000-0001-8904-877XXiang Yu1https://orcid.org/0009-0005-6933-3182School of Economics and Management, Shanghai Polytechnic University, Shanghai, ChinaSchool of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, ChinaThe aluminum laminate pouch of pouch batteries is highly prone to deformation, which can cause various surface defects, thereby affecting their service life and potentially posing safety hazards. To address this problem, we propose an algorithm named YOLOv8-UCB for detecting surface defects in pouch batteries, which is based on the YOLOv8 model. First, while retaining the original network structure, we replaced the backbone of YOLOv8 with the UniRepLKNet feature extraction network to achieve a larger receptive field. Second, we constructed a distributed focal detection head CLLAHead, to better capture the features at different scales. Additionally, after each Concat layer in the head, we incorporated BiFormer attention mechanisms to enhance content-aware perception. Finally, we replaced the loss function with Slide Loss to address the issue of sample classification imbalance. The experimental results indicate that the improved algorithm achieved significant increases in precision, recall, mAP@0.5 and mAP@0.5:0.95, with improvements of 11.7%, 8.2%, 8.9% and 6.5% respectively, over the baseline model. In comparative studies against contemporaneous methodologies, the refined algorithm exhibited pronounced superiority in detection capabilities.https://ieeexplore.ieee.org/document/10807296/Pouch battery defect detectionYOLOv8feature extraction networkdetection headattention mechanismloss function |
| spellingShingle | Hao Hao Xiang Yu YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8 IEEE Access Pouch battery defect detection YOLOv8 feature extraction network detection head attention mechanism loss function |
| title | YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8 |
| title_full | YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8 |
| title_fullStr | YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8 |
| title_full_unstemmed | YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8 |
| title_short | YOLOv8-UCB: Visual Detection of Pouch Battery Using Improved YOLOv8 |
| title_sort | yolov8 ucb visual detection of pouch battery using improved yolov8 |
| topic | Pouch battery defect detection YOLOv8 feature extraction network detection head attention mechanism loss function |
| url | https://ieeexplore.ieee.org/document/10807296/ |
| work_keys_str_mv | AT haohao yolov8ucbvisualdetectionofpouchbatteryusingimprovedyolov8 AT xiangyu yolov8ucbvisualdetectionofpouchbatteryusingimprovedyolov8 |