基于稀疏匹配的机床零件瑕疵投影辅助视觉检测方法

Projection Aided Visual Inspection Method for Machine Tool Parts Defects Based on Sparse Matching

机械臂在自动精准打磨机床零件前需要对机床零件进行特征识别,传统激光点阵扫描精度高,但设备成本高昂而难以规模应用。引入固定式投影产生扫描线图、棋盘格图和随机灰度图等特征作为测量辅助,并结合双目立体视觉测量对零件瑕疵进行检测,改善了传统双目视觉针对光滑平面零件难以识别特征进行立体匹配的情况。分析结果与激光点阵扫描结果进行精度校验。同时,通过5种经典局部立体匹配算法与所提方法进行精度比较,并对辅助图的关键参数进行精度关联分析。最后,引入自适应算法对所提方法进行优化以提高识别精度。结果表明:随机灰度图能够显著提高匹配精度,扫描线优化与子像素拟合能够进一步提高匹配精度,扫描线法与棋盘格法优化后精度提高较大,新方法可有效识别平面零件的瑕疵特征,检测精度达标区域占比大于98%。

The mechanical arm needs to carry out feature recognition for machine tool parts before automatic and accurate grinding of machine tool parts.The traditional laser lattice scanning has high accuracy,but the equipment cost is high and it is difficult to scale application.Fixed projection was introduced to generate scanning line map,checkerboard map and random gray scale map as measurement aids,and binocular stereo vision measurement was combined to detect part defects,by which the stereo matching of traditional binocular vision for the features that were difficult to recognize for smooth plane parts was improved.The accuracy of the analysis results was verified with the laser lattice scanning results.At the same time,the accuracy of five classical local stereo matching algorithms was compared with the proposed method,and the accuracy correlation analysis of the key parameters of the auxiliary image was carried out.Finally,an adaptive algorithm was introduced to optimize the proposed method to improve the recognition accuracy.The results show that the random gray scale image can significantly improve the matching accuracy,the scanning line optimization and sub-pixel fitting can further improve the matching accuracy,and the accuracy of the scanning image and checkerboard method after optimization is greatly improved.The new method can effectively identify the defect features of plane parts,and the detection accuracy reaches the standard area accounting for more than 98%.