基于改进的YOLOv5s的轴承表面缺陷的小目标检测
Haisong Xu1, Xiaolin Shi1, Han Zhang1
1School of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou, China.
PloS one
|July 28, 2025
概括
一个新的ECN-YOLOv5s模型将小型轴承缺陷检测精度提高到92.7%. 这种改进的方法为工业自动化安全提供了更好的特征提取和降低对背景干扰的敏感性.
科学领域:
- 工业自动化 工业自动化
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 精确检测轴承表面的小缺陷 (,伤,划痕) 对于工业设备的安全至关重要.
- 传统的方法在较小的目标和背景干扰方面缺乏足够的特征提取.
研究的目的:
- 提出一个改进的YOLOv5s模型,ECN-YOLOv5s,用于更好地检测小型轴承表面缺陷.
- 解决现有方法的特征提取和背景敏感性的局限性.
主要方法:
- 将一个高效的频道注意力 (ECA) 机制集成到骨干网络中,以进行高效的频道注意力计算.
- 用内容意识重组特征 (CARAFE) 模块取代了最近邻近的提升采样,以改善上下文信息聚合.
- 使用标准化瓦瑟斯坦距离 (NWD) 作为损失函数,以减少对小物体位置偏差的敏感性.
主要成果:
- ECN-YOLOv5s模型在自制数据集上实现了92.7%的平均平均精度 (mAP),用于轴承缺陷识别.
- 与标准YOLOv5s模型相比,mAP的改善率达到了2.4%.
- 在检测小型轴承缺陷方面表现优于其他主流检测模型.
结论:
- 该ECN-YOLOv5s模型有效地解决了检测小型轴承表面缺陷的挑战.
- 拟议的方法符合工业要求,用于准确可靠的缺陷检测,提高设备安全.
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