轴承环的表面缺陷检测基于改进的YOLOv5网络
Haitao Xu1,2, Haipeng Pan1,2, Junfeng Li1,2
1School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了改进的YOLOv5用于轴承表面缺陷检测,达到97.3%的平均平均精度 (mAP) 和每秒100 (FPS) 检测速度. 改进的方法有效地识别了复杂背景上的各种缺陷.
科学领域:
- 工业工程 工业工程 工业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 轴承表面检查对于工业质量控制至关重要.
- 复杂的背景,不同的照明和各种各样的缺陷类型对自动检测构成了挑战.
- 现有的方法通常在真实世界工业数据上的准确性和速度方面扎.
研究的目的:
- 开发用于轴承环的先进表面缺陷检测方法.
- 提高工业环境中自动缺陷识别的准确性和效率.
- 解决当前处理复杂图像特征的方法的局限性.
主要方法:
- 开发了一种改进的YOLOv5模型,包括C2f模块,SPD模块和CARAFE操作员.
- 网络修改旨在减少参数,增强低分辨率图像处理,改善功能利用.
- 创建了一个专门的工业轴承环表面图像数据集,并用于培训和验证.
主要成果:
- 拟议的方法在轴承表面缺陷数据集上实现了97.3%的平均平均精度 (mAP).
- 和黑点缺陷的检测精度分别提高了2.2%和3.9%.
- 该模型显示了每秒100 (FPS) 的高检测速度.
结论:
- 改进的YOLOv5方法显著提高了轴承环表面缺陷检测的准确性和速度.
- 该模型的强度和效率符合工业缺陷检查的要求.
- 这种方法为主流缺陷检测算法提供了优越的替代方案.
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