一种基于改进的YOLOv5的高精度和轻量化方法,用于检测钢表面缺陷
Mudan Zhou1, Haoyu Wang2, Yuhao Wang2
1School of Information Science & Technology, Xiamen University Tan Kah Kee College, Zhangzhou, 363105, China. mudanzhou@xujc.com.
Scientific reports
|December 2, 2025
概括
这项研究介绍了ASFRW-YOLO,这是一个用于检测钢表面缺陷的新系统. 它显著提高了实时工业检查的准确性和速度.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 精确的钢表面缺陷检测对于安全性和效率至关重要.
- 现有的方法面临的挑战是小缺陷,复杂的表面和实时处理.
研究的目的:
- 开发一个增强的YOLOv5模型,ASFRW-YOLO,用于改进钢表面缺陷检测.
- 解决当前系统在准确性,复杂性处理和实时性能方面的局限性.
主要方法:
- 拟议的ASFRW-YOLO模型集成了多尺度ASF模块和RepNCSPELAN4模块.
- 使用的Wise-IoU损失与适应权重用于精细的界限框回归.
- 在NEU-DET数据集上进行了实验,使用8:1:1的列车验证测试分割.
主要成果:
- 实现了83.2%的平均精度在IOU0.5和46.4%在IOU0.5-0.95.5之间.
- 与YOLOv5s.相比,显示了约7个百分点的改善.
- 保持了轻量级设计 (6.20M参数),用于640x640图像的~125 FPS处理.
结论:
- ASFRW-YOLO有效地平衡了检测精度,计算效率和模型紧性.
- 该模型非常适合实时工业缺陷检查应用.
- ASFRW-YOLO提供了一种强大的解决方案,用于检测钢的微妙表面缺陷.
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