机器视觉应用中物体离焦及过厚对测量精度的影响

机器视觉技术在工业测量中具有广泛应用，实现高精度的机器视觉测量对精密加工、制造具有重要意义。针对机器视觉应用中待测物体偏离焦面及在纵深方向过厚导致测量结果出现误差的问题进行了研究。重点讨论了系统中镜头远心度引起的平行性误差及其对离焦物体测量结果的影响。实验结果表明，在物体偏离最佳成像面而引入的误差中，平行性误差占到90%左右，因此可通过后期对平行性误差进行补偿以较大程度地提高系统测量精度。此外，针对待测物体过厚导致边缘模糊这一问题采用多种边缘检测算法进行了分析。结果表明在一定范围内，物体纵深方向越厚，边缘检测误差越大。在这一结果的基础上，对算法进行改进，提出一种基于图像灰度曲线的补偿方法，使测量误差由超过20 μm降至10 μm以下。

Abstract

Machine vision technology is widely used in industrial measurement. To realize high precision measurement of machine vision is of great significance to precision machining and manufacturing. The problem that measuring object will cause measurement error in machine vision application is studied when it deviates from the focal plane or has a certain thickness. The influence of parallelism error caused by lens telecentricity on measurement error of defocus sample is discussed on emphasis. The experimental results show that among all measurement errors caused by sample deviation from the optimal imaging plane, the proportion of parallelism error accounts for about 90% . Measuring accuracy can be improved greatly by compensating parallelism error. Additionally, in order to analyze the problem that the edge is fuzzy when sample has a large thickness, several edge detection algorithms are used. The results show that within a certain range, the thicker the object is, the larger the edge detection error is. On the basis of this result, a compensation method based on image gray-level curve is put forward. As a result, measurement error has decreased from more than 20 μm to less than 10 μm.

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来先家, 徐文东, 赵成强, 肖阳. 机器视觉应用中物体离焦及过厚对测量精度的影响[J]. 光学学报, 2015, 35(8): 0815002. Lai Xianjia, Xu Wendong, Zhao Chengqiang, Xiao Yang. Influence of Sample Defocus and Large Thickness on Measurement Error in Machine Vision Application[J]. Acta Optica Sinica, 2015, 35(8): 0815002.

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