李玉萍, 谢俊, 王子贤, 赵宇凡, 杨启志. 基于机器视觉的微电机摩擦片残缺检测[J]. 制造技术与机床, 2022, (7): 147-151. DOI: 10.19287/j.mtmt.1005-2402.2022.07.025
引用本文: 李玉萍, 谢俊, 王子贤, 赵宇凡, 杨启志. 基于机器视觉的微电机摩擦片残缺检测[J]. 制造技术与机床, 2022, (7): 147-151. DOI: 10.19287/j.mtmt.1005-2402.2022.07.025
LI Yuping, XIE Jun, WANG Zixian, ZHAO Yufan, YANG Qizhi. Defect detection of micromotor friction plate based on machine vision[J]. Manufacturing Technology & Machine Tool, 2022, (7): 147-151. DOI: 10.19287/j.mtmt.1005-2402.2022.07.025
Citation: LI Yuping, XIE Jun, WANG Zixian, ZHAO Yufan, YANG Qizhi. Defect detection of micromotor friction plate based on machine vision[J]. Manufacturing Technology & Machine Tool, 2022, (7): 147-151. DOI: 10.19287/j.mtmt.1005-2402.2022.07.025

基于机器视觉的微电机摩擦片残缺检测

Defect detection of micromotor friction plate based on machine vision

  • 摘要: 应用机器视觉技术对微电机中的摩擦片进行残缺检测,通过选定阈值将摩擦片的灰度图二值化,对二值图像进行连通区域标记,基于目标区域的最小外接矩形的长半轴,提取出摩擦片边界区域;然后使用闭运算消除摩擦片边界区域上的缝隙,再将边界区域转化为亚像素轮廓;最后采用改进的最小二乘法拟合圆,将拟合得到的圆心与半径作为摩擦片的圆心与半径,计算轮廓上所有的像素点到圆心的距离,并找出最小距离,将其与半径进行比较来判断摩擦片是否残缺。通过试验数据分析,该检测算法的准确率达到98%,可代替人工进行检测,能有效降低人力成本、提高检测效率。

     

    Abstract: The friction plate of micromotor is detected to judge whether it is complete by means of machine vision technology. Firstly, grayscale image of the friction plate is binarized according to the selected threshold, and then the connected region of the binary image is marked. Based on major semi axis of the minimum bounding rectangle of the target region, the region of the friction plate boundary is extracted. Secondly, gaps in the region of the friction plate boundary are eliminated through closing operation, then the region of boundary is transformed into sub-pixel contour. Finally, an improved least square method is used to fit the contour to obtain the center and radius of the friction plate. Distances between pixel points and the center of the friction plate are calculated in order to find the minimum distance. The minimum distance is compared with the radius to estimate whether the friction plate is incomplete. The experiment results show that the accuracy of the detection system reaches 98%. Therefore, the system can replace manual detection, effectively reduce labor cost and improve detection efficiency.

     

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