基于改进Snake算法的圆孔类零件测量方法OA

At present,machine vision still faces unsatisfied accuracy in dimensional measurement,so it is difficult to meet the needs of high-precision measurement.In view of this,the study proposed a Snake model optimization method integrating the multi-stage edge enhancement mechanism to improve the visual detection accuracy.The Canny operator is optimized in the aspects of noise suppression,multi-directional gradient feature calculation and threshold segmentation,the edge point set of contour is extracted,and the initial positioning of circular hole contour position is realized.The initial curve setting of the Snake algorithm is improved to further refine the edge of the target area where the circular hole is located.The optimized edge points are screened by integrating the RANSAC(random sample consensus)algorithm,so that the abnormal points are removed.Finally,the center and radius of the circle are further fitted accurately by the least square method,so as to realize the high-precision detection and measurement of the circular hole.The experiments show that the system can measure the workpiece with a precision of 0.02 mm,and the measurement accuracy is significantly higher than that of the traditional Snake algorithm,so the proposed algorithm can meet the detection requirements of industrial high-precision circular hole parts.