An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole
Focus on the requirement for detecting laser welding defects of lithium battery pole, a new model based on the improved YOLOv5 algorithm was proposed in this paper. First, all the 3 × 3 convolutional kernels in the backbone network were replaced by 6 × 6 convolutional kernels to improve the model’s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/4/2402 |
| _version_ | 1797622535192313856 |
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| author | Yatao Yang Yunhao Zhou Nasir Ud Din Junqing Li Yunjie He Li Zhang |
| author_facet | Yatao Yang Yunhao Zhou Nasir Ud Din Junqing Li Yunjie He Li Zhang |
| author_sort | Yatao Yang |
| collection | DOAJ |
| description | Focus on the requirement for detecting laser welding defects of lithium battery pole, a new model based on the improved YOLOv5 algorithm was proposed in this paper. First, all the 3 × 3 convolutional kernels in the backbone network were replaced by 6 × 6 convolutional kernels to improve the model’s detection capability of a small defect; second, the last layer of the backbone network was replaced by our designed SPPSE module to enhance the detection accuracy of the model; then the improved RepVGG module was introduced in the head network, which can help to improve the inference speed of the model and enhance the feature extraction capability of the network; finally, SIOU was used as the bounding box regression loss function to improve the accuracy and training speed of the model. The experimental results show that our improved YOLOv5 model achieved 97% mAP and 270 fps on our dataset. Compared with conventional methods, ours had the best results. The ablation experiments were conducted on the publicly available datasets PASCAL VOC and MS COCO, and their mAP@0.5 was improved by 2.4% and 3%, respectively. Additionally, our model improved the average detection rate for small targets on the MS COCO dataset by 2.4%, showing that it can effectively detect small target defects. |
| first_indexed | 2024-03-11T09:11:41Z |
| format | Article |
| id | doaj.art-19a0e39ed3d847929eba460fefe4d5bb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:11:41Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-19a0e39ed3d847929eba460fefe4d5bb2023-11-16T18:55:15ZengMDPI AGApplied Sciences2076-34172023-02-01134240210.3390/app13042402An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery PoleYatao Yang0Yunhao Zhou1Nasir Ud Din2Junqing Li3Yunjie He4Li Zhang5College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen 518000, ChinaFocus on the requirement for detecting laser welding defects of lithium battery pole, a new model based on the improved YOLOv5 algorithm was proposed in this paper. First, all the 3 × 3 convolutional kernels in the backbone network were replaced by 6 × 6 convolutional kernels to improve the model’s detection capability of a small defect; second, the last layer of the backbone network was replaced by our designed SPPSE module to enhance the detection accuracy of the model; then the improved RepVGG module was introduced in the head network, which can help to improve the inference speed of the model and enhance the feature extraction capability of the network; finally, SIOU was used as the bounding box regression loss function to improve the accuracy and training speed of the model. The experimental results show that our improved YOLOv5 model achieved 97% mAP and 270 fps on our dataset. Compared with conventional methods, ours had the best results. The ablation experiments were conducted on the publicly available datasets PASCAL VOC and MS COCO, and their mAP@0.5 was improved by 2.4% and 3%, respectively. Additionally, our model improved the average detection rate for small targets on the MS COCO dataset by 2.4%, showing that it can effectively detect small target defects.https://www.mdpi.com/2076-3417/13/4/2402laser welding defectlithium battery poleloss functionRepVGGYOLOv5 |
| spellingShingle | Yatao Yang Yunhao Zhou Nasir Ud Din Junqing Li Yunjie He Li Zhang An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole Applied Sciences laser welding defect lithium battery pole loss function RepVGG YOLOv5 |
| title | An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole |
| title_full | An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole |
| title_fullStr | An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole |
| title_full_unstemmed | An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole |
| title_short | An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole |
| title_sort | improved yolov5 model for detecting laser welding defects of lithium battery pole |
| topic | laser welding defect lithium battery pole loss function RepVGG YOLOv5 |
| url | https://www.mdpi.com/2076-3417/13/4/2402 |
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