Detection of Welding Defects Tracked by YOLOv4 Algorithm
The recall rate of the original YOLOv4 model for detecting internal defects in aluminum alloy welds is relatively low. To address this issue, this paper introduces an enhanced model, YOLOv4-cs1. The improvements include optimizing the stacking method of residual blocks, modifying the activation func...
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MDPI AG
2025-02-01
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| author | Yunxia Chen Yan Wu |
| author_facet | Yunxia Chen Yan Wu |
| author_sort | Yunxia Chen |
| collection | DOAJ |
| description | The recall rate of the original YOLOv4 model for detecting internal defects in aluminum alloy welds is relatively low. To address this issue, this paper introduces an enhanced model, YOLOv4-cs1. The improvements include optimizing the stacking method of residual blocks, modifying the activation functions for different convolutional layers, and eliminating the downsampling layer in the PANet (Pyramid Attention Network) to preserve edge information. Building on these enhancements, the YOLOv4-cs2 model further incorporates an improved Spatial Pyramid Pooling (SPP) module after the third and fourth residual blocks. The experimental results demonstrate that the recall rates for pore and slag inclusion detection using the YOLOv4-cs1 and YOLOv4-cs2 models increased by 28.9% and 16.6%, and 45% and 25.2%, respectively, compared to the original YOLOv4 model. Additionally, the mAP values for the two models are 85.79% and 87.5%, representing increases of 0.98% and 2.69%, respectively, over the original YOLOv4 model. |
| format | Article |
| id | doaj-art-241de11ea3e141d98c8bb7274ae4f0d7 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-241de11ea3e141d98c8bb7274ae4f0d72025-08-20T03:12:08ZengMDPI AGApplied Sciences2076-34172025-02-01154202610.3390/app15042026Detection of Welding Defects Tracked by YOLOv4 AlgorithmYunxia Chen0Yan Wu1School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, ChinaSchool of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai 201209, ChinaThe recall rate of the original YOLOv4 model for detecting internal defects in aluminum alloy welds is relatively low. To address this issue, this paper introduces an enhanced model, YOLOv4-cs1. The improvements include optimizing the stacking method of residual blocks, modifying the activation functions for different convolutional layers, and eliminating the downsampling layer in the PANet (Pyramid Attention Network) to preserve edge information. Building on these enhancements, the YOLOv4-cs2 model further incorporates an improved Spatial Pyramid Pooling (SPP) module after the third and fourth residual blocks. The experimental results demonstrate that the recall rates for pore and slag inclusion detection using the YOLOv4-cs1 and YOLOv4-cs2 models increased by 28.9% and 16.6%, and 45% and 25.2%, respectively, compared to the original YOLOv4 model. Additionally, the mAP values for the two models are 85.79% and 87.5%, representing increases of 0.98% and 2.69%, respectively, over the original YOLOv4 model.https://www.mdpi.com/2076-3417/15/4/2026weld defectsdeep learningtarget detectionYOLOv4 |
| spellingShingle | Yunxia Chen Yan Wu Detection of Welding Defects Tracked by YOLOv4 Algorithm Applied Sciences weld defects deep learning target detection YOLOv4 |
| title | Detection of Welding Defects Tracked by YOLOv4 Algorithm |
| title_full | Detection of Welding Defects Tracked by YOLOv4 Algorithm |
| title_fullStr | Detection of Welding Defects Tracked by YOLOv4 Algorithm |
| title_full_unstemmed | Detection of Welding Defects Tracked by YOLOv4 Algorithm |
| title_short | Detection of Welding Defects Tracked by YOLOv4 Algorithm |
| title_sort | detection of welding defects tracked by yolov4 algorithm |
| topic | weld defects deep learning target detection YOLOv4 |
| url | https://www.mdpi.com/2076-3417/15/4/2026 |
| work_keys_str_mv | AT yunxiachen detectionofweldingdefectstrackedbyyolov4algorithm AT yanwu detectionofweldingdefectstrackedbyyolov4algorithm |