基于激光再制造机器人的表面浅斑缺陷识别研究

机器视觉系统对于提高激光机器人再制造质量有重要作用。针对浅斑类缺陷无法在点云数据中定位的问题,开发了专用识别系统,将二维图片和三维点云结合起来实现该类缺陷的三维检测。对灰度图片进行处理,包括图像频域增强、区域标记和区域合并等,实现了二维图片中的缺陷定位;利用双目立体视觉系统对再制造零件表面进行扫描,获取零件表面三维点云数据,同时将二维缺陷边界转换为三维缺陷边界,实现了点云数据中缺陷的定位。实验结果表明,该系统能有效识别浅斑类缺陷,并且再制造精度高。

Research on Shallow Spot Defect Detection Based on Laser Remanufacturing Robot

Machine vision system plays an important role in improving the quality of laser robot system. A system to detect three-dimensional (3D) shallow spot defects by combining two-dimensional (2D) images with 3D point clouds is carried out, which solves the problem of 3D positioning of shallow spot defects. With gray image processing, including image enhancement in the frequency domain, region marking and region merging, the system achieves the 2D positioning of defects. In order to obtain the 3D point cloud, laser remanufacturing part surfaces are scanned with binocular stereo vision system, and the 2D defect boundaries are then converted into 3D ones to realize the positioning of defects in the point cloud. Experimental results show that the system can effectively identify shallow spot defects, which further enables high precision remanufacturing of parts.