Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model
Abstract This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. The key innovations of SLF-YOLO include a novel SC_C2f module with a channel...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94936-9 |
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| author | Yuan Liu Yilong Liu Xiaoyan Guo Xi Ling Qingyi Geng |
| author_facet | Yuan Liu Yilong Liu Xiaoyan Guo Xi Ling Qingyi Geng |
| author_sort | Yuan Liu |
| collection | DOAJ |
| description | Abstract This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. The key innovations of SLF-YOLO include a novel SC_C2f module with a channel gating mechanism to enhance feature representation and regulate information flow, and a newly designed Light-SSF_Neck structure to improve multi-scale feature fusion and morphological feature extraction. Additionally, an improved FIMetal-IoU loss function is introduced to boost generalization performance, particularly for fine-grained and small-target defects. Experimental results demonstrate that SLF-YOLO achieves a mean Average Precision (mAP) of 80.0% on the NEU-DET dataset, outperforming YOLOv8’s 75.9%. On the AL10-DET dataset, SLF-YOLO achieves a mAP of 86.8%, striking an effective balance between detection accuracy and computational efficiency without increasing model complexity. Compared to other mainstream models, SLF-YOLO demonstrates strong detection accuracy while maintaining a lightweight architecture, making it highly suitable for industrial applications in metal surface defect detection. The source code is available at https://github.com/zacianfans/SLF-YOLO . |
| format | Article |
| id | doaj-art-48ae106abb8d447495b76718c29d5f8a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-48ae106abb8d447495b76718c29d5f8a2025-08-20T03:07:41ZengNature PortfolioScientific Reports2045-23222025-04-0115112010.1038/s41598-025-94936-9Metal surface defect detection using SLF-YOLO enhanced YOLOv8 modelYuan Liu0Yilong Liu1Xiaoyan Guo2Xi Ling3Qingyi Geng4School of Mathematics, Northwest UniversitySchool of Mathematics, Northwest UniversitySchool of Mathematics, Northwest UniversitySchool of Mathematics, Northwest UniversitySchool of Mathematics, Northwest UniversityAbstract This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. The key innovations of SLF-YOLO include a novel SC_C2f module with a channel gating mechanism to enhance feature representation and regulate information flow, and a newly designed Light-SSF_Neck structure to improve multi-scale feature fusion and morphological feature extraction. Additionally, an improved FIMetal-IoU loss function is introduced to boost generalization performance, particularly for fine-grained and small-target defects. Experimental results demonstrate that SLF-YOLO achieves a mean Average Precision (mAP) of 80.0% on the NEU-DET dataset, outperforming YOLOv8’s 75.9%. On the AL10-DET dataset, SLF-YOLO achieves a mAP of 86.8%, striking an effective balance between detection accuracy and computational efficiency without increasing model complexity. Compared to other mainstream models, SLF-YOLO demonstrates strong detection accuracy while maintaining a lightweight architecture, making it highly suitable for industrial applications in metal surface defect detection. The source code is available at https://github.com/zacianfans/SLF-YOLO .https://doi.org/10.1038/s41598-025-94936-9Surface defect detectionYOLOv8Lightweight modelMulti-scale fusionOptimized loss function |
| spellingShingle | Yuan Liu Yilong Liu Xiaoyan Guo Xi Ling Qingyi Geng Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model Scientific Reports Surface defect detection YOLOv8 Lightweight model Multi-scale fusion Optimized loss function |
| title | Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model |
| title_full | Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model |
| title_fullStr | Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model |
| title_full_unstemmed | Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model |
| title_short | Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model |
| title_sort | metal surface defect detection using slf yolo enhanced yolov8 model |
| topic | Surface defect detection YOLOv8 Lightweight model Multi-scale fusion Optimized loss function |
| url | https://doi.org/10.1038/s41598-025-94936-9 |
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