EAD-YOLOv10: Lightweight Steel Surface Defect Detection Algorithm Research Based on YOLOv10 Improvement
In response to the issues of low detection accuracy (DA), slow speed, and missed detections caused by the complex texture background and diverse shapes of surface defects (SD) in steel, this paper designs an improved lightweight YOLOv10 model called EAD-YOLOv10. By incorporating an Adaptive Downsamp...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2025-01-01
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| coleção: | IEEE Access |
| Assuntos: | |
| Acesso em linha: | https://ieeexplore.ieee.org/document/10930899/ |
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