基于改进的YOLOV5进行轴承表面痕检测的研究
Huakun Jia1, Huimin Zhou1, Zhehao Chen1
1College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究介绍了YOLOV5-CDG,这是一个改进的AI模型,用于检测轴承表面的划痕. 它实现了高精度和速度,超过了工业检查的标准方法.
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:
- 手动检查轴承是耗时的,不能满足工业需求.
- 自动抓痕检测对于确保机器可靠性和高通过率至关重要.
- 现有的方法缺乏实时工业应用所需的速度和准确性.
研究的目的:
- 开发一种先进的AI模型,以准确有效地检测轴承表面的划痕.
- 改进现有的对象检测网络,用于工业缺陷识别.
- 建立用于自动化轴承表面检查的机器视觉系统.
主要方法:
- 开发了一种改进的YOLOV5网络,称为YOLOV5-CDG.
- YOLOV5-CDG集成了协调注意力 (CA) 机制,可变形卷积网络 (DCN) 和GhostNet.
- 机器视觉系统和定制数据集被用于培训和验证.
主要成果:
- YOLOV5-CDG模型在痕检测方面实现了97%的平均精度 (AP),超过YOLOV5的3.4%.
- 该模型在分类有缺陷和合格产品方面显示了99.46%的准确性.
- 每张图像的检测时间在CPU上为263.4ms,在GPU上为12.2ms.
结论:
- 拟议的YOLOV5-CDG型号为轴承表面划痕检测提供了更高的速度和精度.
- 开发的系统满足了对轴承缺陷的自动检查的工业要求.
- 这项研究有助于加强机械和设备制造业的质量控制.
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