对于复杂背景下微小圆柱体芯块的端面缺陷检测,受表面镀层不均匀造成的灰度信息的干扰,现有的基于阈值、形态学、边缘Canny的分割方法的检测具有不精确性。提出一种基于机器视觉的结构光三角剖分技术缺陷检测算法。首先,采用结构光主动视觉检测法获取两幅含有一定缺陷信息的图像;然后,根据模板匹配、形态学分析、频率统计将缺陷轮廓粗略勾画出来;最后,使用Delaunay三角剖分技术实现端面缺陷区域的精确勾画。实验结果表明,该方法对隐藏于杂乱背景下的缺陷检测具有较高的识别率,重复精度可达亚像素级,并且抗干扰性好、实用性强、效率高。

For detection of the end face defects of small cylinder core with complex image background, the current detection methods based on threshold, morphological and edge Canny are inaccurate due to the interference of the gray information caused by the unevenness of the surface coating. We present a defect detection algorithm based on machine vision for optical triangulation. Firstly, two images with certain defect information are obtained based on the active light visual inspection method. Then, the defect contours are sketched out according to the template matching, morphological analysis and frequency statistics. Finally, we use Delaunay triangulation technique to realize the accurate sketch of the defect area. The experimental results show that this method has high accuracy and robustness for defect detection hidden in complex background, and the repeatability can reach sub-pixel level, with good anti-jamming, strong practicability and high efficiency.