针对工业测量零件尺寸中存在测量精度低、算法适应性差等问题,设计了基于超分模型与改进Canny算法的机器视觉零件测量系统。首先,对相机进行尺寸标定,得到像素距离与实际尺寸的比例关系;其次,引入并改进超分模型对输入图片进行预处理,得到边缘细节更加清晰的图像;接着,为了优化工业测量环境中光照条件差导致的边缘检测问题,结合Otsu算法与双阈值分割算法,利用局部阈值分割的方式改进Canny边缘滤波算法得到边缘图像;最后,对边缘图像进行圆拟合与直线拟合后,根据相机标定结果与超分模型参数标记零件尺寸结果。实验结果表明,本系统的测量精度可以达到0.001 mm,平均测量误差为0.007 4 mm,整体测量速度为6.44 s,能够满足高精度工业测量需求。

In order to solve the problems of low measurement accuracy and poor algorithm adaptability in industrial measurement of part dimensions,a machine vision parts measurement system based on super-resolution model and improved Canny algorithm is designed and proposed.Firstly,the camera is calibrated to obtain the proportional relationship between the pixel distance and the actual size.Secondly,a super-resolution model is introduced and improved to pre-process the input image to obtain an image with clearer edge details.Thirdly,in order to solve the edge detection problem caused by poor lighting conditions in the industrial measurement environment,the Otsu algorithm and the double threshold segmentation algorithm are combined,and the Canny edge filtering algorithm is improved by using local threshold segmentation to obtain edge images.Finally,after simultaneously performing circle fitting and straight line fitting on the edge image,the part size results are marked based on the camera calibration results and super-resolution model parameters.The experiment results show that the measurement accuracy of this system can reach 0.001 mm,the average measurement error is 0.007 4 mm,and the overall measurement speed is 6.44 s,which can meet the needs of high-precision industrial measurement.