基于奇异值分解的橡胶密封圈表面缺陷检测方法

为解决橡胶密封圈表面缺陷人工检测效率低，缺陷提取困难等问题，提高橡胶密封圈缺陷在线检测速度及准确率，提出一种基于机器视觉的橡胶密封圈表面缺陷检测方法。该方法采用多相机多线程图像采集模式，采集橡胶密封圈的上下表面不同位置的局部图像；对图像自适应中值滤波后进行边缘增强，并使用高斯差分算子提取轮廓粗边缘，利用Zernike 矩获取亚像素边缘位置；针对边缘存在不连续点问题，使用Ceres库多项式拟合，估计断点位置，并更新所有边缘位置；根据边缘位置寻找出整张图像中橡胶密封圈表面图像区域，并将该环形兴趣区域映射到矩形区域中；将获得的图像进行奇异值分解（SVD），并通过连通域分析，提取出图像中的奇异区域，即存在缺陷的位置。经实验验证，基于奇异值分解的橡胶圈表面缺陷检测方法鲁棒性好、效率高，可以快速准确地寻找出橡胶密封圈表面缺陷信息。

Surface Defect Detection Method of Rubber Sealing Ring Based on Singular Value Decomposition

In order to solve the problems of low manual detection efficiency and difficult extraction of rubber ring surface defects,and improve the on-line detection speed and accuracy of rubber ring defects,a machine vision based rubber ring surface defect detection method was proposed.In this method,the multi-camera multi-thread image acquisition mode was used to collect local images at different positions on the upper and lower surfaces of the rubber ring.The image was adaptively median-filtered and then edge-enhanced.The Gaussian difference operator was used to extract the rough edges of the contour,and the Zernike moment was used to obtain the sub-pixel edge position.The Ceres library polynomial fitting was used to estimate the position of the breakpoint and update all the edge positions for the problem of discontinuous points on the edge.The image area of the rubber ring surface in the whole image was obtained according to the edge position,and the ring region of interest was mapped into the rectangular region.Singular value decomposition (SVD) was performed on the obtained image,and through connected domain analysis,the singularity in the image,that is,the location of defects,was extracted.The experimental results show that the rubber ring surface defect detection method based on singular value decomposition is robust and efficient,which can quickly and accurately find the surface defect information of the rubber ring.