GRP-YOLOv5:基于YOLOv5的改进轴承缺陷检测算法
Yue Zhao1, Bolun Chen1,2, Bushi Liu1
1Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了GRP-YOLOv5,这是一种用于检测化学传输设备轴承缺陷的增强算法. 改进的模型实现了高精度,但需要进一步优化实时应用程序.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 化学传输设备的轴承缺陷导致了严重的生产低效率.
- 当前的检测方法可能会与缺陷和正常区域之间的视觉相似性作斗争.
研究的目的:
- 开发一个改进的轴承缺陷检测算法,以提高准确性和可靠性.
- 解决现有的缺陷检测方法的局限性.
主要方法:
- 提出了基于YOLOv5 (GRP-YOLOv5) 的改进轴承缺陷检测算法.
- 图像预处理涉及马转换以增强对比度和灰度.
- 使用ResC2Net进行特征提取,以改善细节和语义信息捕获.
- 集成了PConv卷积功能融合,以增加网络深度.
主要成果:
- 该GRP-YOLOv5模型实现了平均平均精度 (mAP@0.5) 的93.5%和mAP@0.5:0.95的52.7%.
- 与其他实验模型相比,该模型在轴承缺陷检测方面表现出卓越的准确性.
- 模型大小优化为25 MB,但每秒 (FPS) 性能被认为是一个限制.
结论:
- GRP-YOLOv5模型为准确的轴承缺陷检测提供了一个有前途的解决方案.
- 需要进一步的研究来优化模型的处理速度,以实时应用.
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