李晓磊, 潘晋孝, 刘宾, 陈平, 魏交统. 装配同轴度的局部成像检测算法研究[J]. 应用光学, 2016, 37(1): 96-99. DOI: 10.5768/JAO201637.0103005
引用本文: 李晓磊, 潘晋孝, 刘宾, 陈平, 魏交统. 装配同轴度的局部成像检测算法研究[J]. 应用光学, 2016, 37(1): 96-99. DOI: 10.5768/JAO201637.0103005
Li Xiao-lei, Pan Jin-xiao, Liu Bin, Chen Ping, Wei Jiao-tong. Detection algorithm of assembly coaxality in local imaging[J]. Journal of Applied Optics, 2016, 37(1): 96-99. DOI: 10.5768/JAO201637.0103005
Citation: Li Xiao-lei, Pan Jin-xiao, Liu Bin, Chen Ping, Wei Jiao-tong. Detection algorithm of assembly coaxality in local imaging[J]. Journal of Applied Optics, 2016, 37(1): 96-99. DOI: 10.5768/JAO201637.0103005

装配同轴度的局部成像检测算法研究

Detection algorithm of assembly coaxality in local imaging

  • 摘要: 在局部成像检测过程中,由于复杂零件外形轮廓或放置状态的不同,使得零件与成像面坐标轴之间产生了一定的夹角,造成获取的对称点集中存在非对称点集或对称点不存在的问题,若采用传统Hough变换、拟合法检测装配同轴度存在较大误差。针对上述问题,提出了装配同轴度的局部成像检测算法,提取图像的上下边缘点集,结合Hough线性变换,统计两点集对投影到霍夫空间的参数空间点,并搜索其累积数量的最大值点,该点对应的对称轴即为最优对称轴。仿真结果表明,该方法可以高精度地提取最优对称轴,同轴度误差仅为0.002 7。因此,采用装配同轴度的局部成像检测方法是有效可行的。

     

    Abstract: In the process of local imaging detection, due to the difference of parts with complex outline or placement state, the parts and image plane coordinates have a certain angle, which can cause the problem that there are asymmetric point sets or no symmetric points in the acquired symmetric sets. When using traditional Hough transform or fitting method to detect assembly coaxality, it has much error. A detection algorithm of assembly coaxality in local imaging was proposed to solve the above problem.This algorithm firstly extracts the upper and lower edge point sets and then counts the parameter space points projecting in Hough space from two point sets based on Hough linear transform. Finally, it searches the maximum point of parameter points cumulative number, the point corresponds to the symmetric axis which is the optimal symmetric axis. The simulated result shows that the method can extract the optimal symmetric axis with high accuracy, and the coaxality error is only 0.002 7 . Therefore, the algorithm of assembly coaxality in local imaging is feasible.

     

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