摘要
In the non-contact measurement using the linear structured light(LSL),the extraction precision of the light stripe center directly affects the measurement accuracy of the whole detection system.To solve the problem that general algorithms cannot accurately extract the center of the light stripe with the uneven width and unstable greyvalue distribution,an adaptive optimization method is proposed.In this method,the stripe region is firstly segmented,and the widths of the laser stripe are calculated by boundary detection.The initial stripe center points are computed by the quadratic weighted grayscale centroid method based on the self-adaptive stripe width.After that,these center points are optimized according to the determined slope threshold.The sub-pixel coordinates of these center points are recalculated.Detailed analysis is also performed in line with the proposed evaluation index of the extraction algorithm.The experimental results show that the mean square error of extracted center points is only 0.1 pixel,meaning that the accuracy of laser stripe center extraction is improved significantly by the method.Furthermore,the method can run effectively at a relatively low computational time cost,and can demonstrate great robustness as well.
在采用线性结构光(Linear structured light,LSL)方式的非接触式测量中,光条纹中心的提取精度直接影响整个检测系统的测量精度。针对通用算法无法准确提取宽度不均匀、灰度值分布不均匀的条纹中心的问题,本文提出了一种自适应优化方法。在该方法中,首先分割条纹区域,通过边界检测来计算激光条纹的宽度。通过基于自适应条纹宽度的二次加权灰度质心法计算初始条纹中心点。之后,根据确定的斜率阈值优化这些中心点,重新计算并获得这些中心点的子像素坐标,同时根据提出的提取算法评估指标对算法进行了详细分析。实验中,提取的中心点的均方误差仅为0.1个像素,结果表明该方法可以显著提高激光条纹中心点的提取精度。此外,该方法可以以相对较低的计算时间有效地运行,鲁棒性良好。
基金
the National Natural Science Foundation of China(No.51975293)
the Aeronautical Science Foundation of China(No.2019ZD052010)。
