基于机器视觉的轴承防尘盖表面缺陷检测

Bearing shield surface defect detection based on machine vision

为实现工业现场中轴承防尘盖表面缺陷的自动检测,提出一种基于机器视觉技术的检测方法。采用蓝色同轴光源作为检测系统所用光源,克服金属反光;采用最小二乘法拟合轴承外圆,根据轴承型号比例分割出防尘盖区域,利用Otsu阈值分割和Roberts边缘提取处理图像,每2°统计值为1的点的数目,与模板轴承此数据比较,求出相差角度,由此将防尘盖字符、非字符区域分离,两部分是否存在缺陷分开判别,互不干扰。实际测试表明:检测系统采集到的轴承图像清晰,缺陷检测算法正确率在96%以上,可实现轴承防尘盖表面缺陷的自动检测。

To realize the automatic detection of bearing shield surface, this paper proposes a method based on machine vision. It uses the blue coaxial light to overcome the metal reflection;it uses the least squares method to fit the bearing outer circle. According to the bearing type, it segments the bearing. Using Otsu’s method and Roberts edge extraction it processes the shield image. It calculates the points when value is 1 per 2＆#176;. Compared with the template data, it obtains the phase angle, then separates the character region and no-character region;there is no interference when the two parts defect is detected. Experiments show that：the captured image is unambiguous and the correct rate of detection algorithm is more than 96%. It can realize the automatic detection of bearing shield surface.