基于视觉技术的非接触测量精度优化研究

针对精密加工行业零件形位公差在线检测实时性不高并且无法同时检测多个零件的问题，采用机器视觉技术，改良了相机采集图像的图像预处理流程与测量方法，提出一种基于CNN的超分辨重建的非接触测量改良算法。相较于其他超分辨率重建算法该算法模型简单，精度较高，速度快，在资源受限的情况下可以兼顾测量精度和效率。为了验证所设计算法的可靠性，设计了一套机器视觉的非接触测量系统。实验结果表明，改良测量方法后测量精度较之前使用的测量方法至少可提高47.86%,平均提高49.67%;该超分辨率算法在分辨率一定的基础上，对原始采集图像的超分辨率重建提升图像分辨率后，测量精度较不使用超分辨率重建提高了60.38%,最后利用该算法实现对多个目标在线同步测量分析，并且精度不低于同分辨率下单一零件检测精度。

Research on Optimization of non-contact measurement accuracy based on vision technology

To solve the problem that the on-line detection of shape and position tolerance of parts in precision machining industry is not real-time and can not detect multiple parts at the same time, the image preprocessing process and measurement method of images collected by camera are improved by using machine vision technology, and an improved non-contact measurement algorithm based on CNN super-resolution reconstruction is proposed. Compared with other super-resolution reconstruction algorithms, the algorithm has the advantages of simple model, high precision and fast speed. It can take into account the measurement accuracy and efficiency under the condition of limited resources. In order to verify the reliability of the designed algorithm, a non-contact measurement system based on machine vision is designed. The experimental results show that the accuracy of the improved measurement method can be improved by at least 47.86% and 49.67% on average compared with the previous measurement method. The super-resolution algorithm on the basis of the resolution must, to the original acquisition of image super-resolution reconstruction after improve image resolution, measurement accuracy is increased by 60.38%, do not use the super-resolution reconstruction using the algorithm for multiple targets online synchronous measurement analysis, and the precision is not lower than with single parts under resolution precision.