LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminations
Defect inspection of the surface in ultrasonically welded wire terminations is an important inspection procedure to ensure welding quality. However, the detection task of ultrasonic welding defects based on deep learning still faces the challenges of low detection accuracy and slow inference speed....
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098624002829 |
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| author | Jianshu Xu Lun Zhao Yu Ren Zhigang Li Zeshan Abbas Lan Zhang Md Shafiqul Islam |
| author_facet | Jianshu Xu Lun Zhao Yu Ren Zhigang Li Zeshan Abbas Lan Zhang Md Shafiqul Islam |
| author_sort | Jianshu Xu |
| collection | DOAJ |
| description | Defect inspection of the surface in ultrasonically welded wire terminations is an important inspection procedure to ensure welding quality. However, the detection task of ultrasonic welding defects based on deep learning still faces the challenges of low detection accuracy and slow inference speed. Therefore, to solve the above problems, we propose a fast and effective lightweight detection model based on You Only Look Once v8 (YOLOv8n), named LightYOLO. Specifically, first, to achieve fast feature extraction, a Two-Convolution module with FasterNet block and Efficient multi-scale attention (CTFE) structures is introduced in the backbone network. Secondly, Group-Shuffle Convolution (GSConv) is used to construct the feature fusion structure of the neck, which enhances the fusion efficiency of multi-level features. Finally, an auxiliary head training method is introduced to extract shallow details of the network. To verify the effectiveness of the proposed method, we constructed a surface defect data set of ultrasonic welding wire terminals and conducted a series of experiments. The results of experiments show that the precision of LightYOLO is 93.4%, which is 3.5% higher than YOLOv8n(89.9%). In addition, the model size was reduced to 1/2 of the baseline model. LightYOLO shows the potential for rapid detection on edge computing devices. The source code and dataset for our project is accessible at https://github.com/JianshuXu/LightYOLO. |
| format | Article |
| id | doaj-art-62691ef5cf394f39bb835226a96bee93 |
| institution | OA Journals |
| issn | 2215-0986 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-62691ef5cf394f39bb835226a96bee932025-08-20T02:37:29ZengElsevierEngineering Science and Technology, an International Journal2215-09862024-12-016010189610.1016/j.jestch.2024.101896LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminationsJianshu Xu0Lun Zhao1Yu Ren2Zhigang Li3Zeshan Abbas4Lan Zhang5Md Shafiqul Islam6School of Mechanical and Electrical Engineering, Yunnan Open University, Kunming, 650500, China; School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, ChinaSchool of Mechanical and Electrical Engineering, Yunnan Open University, Kunming, 650500, China; Department of Mechanical Engineering, Blekinge Institute of Technology, Karlskrona, 37179, Sweden; Corresponding author at: School of Mechanical and Electrical Engineering, Yunnan Open University, Kunming, 650500, China.School of Mechanical and Electrical Engineering, Yunnan Open University, Kunming, 650500, China; School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, ChinaSchool of Mechanical and Electrical Engineering, Yunnan Open University, Kunming, 650500, ChinaSchool of Mechanical and Electrical Engineering, Yunnan Open University, Kunming, 650500, ChinaDepartment of Mechanical Engineering, Blekinge Institute of Technology, Karlskrona, 37179, SwedenDefect inspection of the surface in ultrasonically welded wire terminations is an important inspection procedure to ensure welding quality. However, the detection task of ultrasonic welding defects based on deep learning still faces the challenges of low detection accuracy and slow inference speed. Therefore, to solve the above problems, we propose a fast and effective lightweight detection model based on You Only Look Once v8 (YOLOv8n), named LightYOLO. Specifically, first, to achieve fast feature extraction, a Two-Convolution module with FasterNet block and Efficient multi-scale attention (CTFE) structures is introduced in the backbone network. Secondly, Group-Shuffle Convolution (GSConv) is used to construct the feature fusion structure of the neck, which enhances the fusion efficiency of multi-level features. Finally, an auxiliary head training method is introduced to extract shallow details of the network. To verify the effectiveness of the proposed method, we constructed a surface defect data set of ultrasonic welding wire terminals and conducted a series of experiments. The results of experiments show that the precision of LightYOLO is 93.4%, which is 3.5% higher than YOLOv8n(89.9%). In addition, the model size was reduced to 1/2 of the baseline model. LightYOLO shows the potential for rapid detection on edge computing devices. The source code and dataset for our project is accessible at https://github.com/JianshuXu/LightYOLO.http://www.sciencedirect.com/science/article/pii/S2215098624002829Ultrasonic metal weldingDeep learningObject detectionLightweight |
| spellingShingle | Jianshu Xu Lun Zhao Yu Ren Zhigang Li Zeshan Abbas Lan Zhang Md Shafiqul Islam LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminations Engineering Science and Technology, an International Journal Ultrasonic metal welding Deep learning Object detection Lightweight |
| title | LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminations |
| title_full | LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminations |
| title_fullStr | LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminations |
| title_full_unstemmed | LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminations |
| title_short | LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminations |
| title_sort | lightyolo lightweight model based on yolov8n for defect detection of ultrasonically welded wire terminations |
| topic | Ultrasonic metal welding Deep learning Object detection Lightweight |
| url | http://www.sciencedirect.com/science/article/pii/S2215098624002829 |
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