基于改进的YOLOv5算法进行轴承缺陷检测
Kangning Li1, Peigang Jiao1, Jiaming Ding1
1School of Construction Machinery, Shandong Jiaotong University, Jinan, Shandong Province, China.
PloS one
|October 28, 2024
概括
这项研究引入了一种改进的YOLOv5模型,用于有效检测轴承缺陷. 改进的方法准确地识别了小型和重叠的缺陷,改进了现有技术的实际应用.
科学领域:
- 机械工程 机械工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 手动轴承检查效率低下,容易错过小缺陷或重叠缺陷.
- 现有的物体检测方法难以应对轴承缺陷识别的复杂性.
研究的目的:
- 开发一种改进的YOLOv5物体检测方法,用于增强轴承缺陷检测.
- 为了应对在轴承中检测小,重叠和多个共存缺陷的挑战.
主要方法:
- 用Res2Block模块取代YOLOv5的C3模块,以获得更优质的功能提取.
- 集成了一种双向特征金字塔网络 (BiFPN),以改善特征融合.
- 对现有的缺陷检测算法进行了废除和比较实验.
主要成果:
- 改进的YOLOv5算法显示了高平均精度 (mAP) 和准确性.
- 在复杂的场景中,可以精确识别轴承上的小目标缺陷.
- 超越了现有的方法,包括那些专门用于小目标检测的方法.
结论:
- 增强的YOLOv5模型为自动轴承缺陷检测提供了更有效的解决方案.
- 为需要精确缺陷识别的实用工业应用提供了宝贵的参考资料.
- 在具有具有挑战性缺陷特征的场景中显著提高检测能力.
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