基于超分模型与改进Canny算法的零件测量系统OACSTPCD

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 algo-rithm 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.