Edge extraction algorithm of packaging robot based on Canny operator

In order to improve the positioning accuracy of the robot's vision system and reduce the influence of redundant information and noise on the image edge, the Canny operator based on the hybrid frog jump optimization algorithm is applied to the edge detection of the robot's paper mounting. In this paper, the maximum interclass variance is used as the objective function of the hybrid leapfrog algorithm, and the Canny operator double threshold is adaptively iteratively optimized to realize the effective extraction of papered lines. The simulation results show that the optimized Canny operator of the algorithm can better detect the boundary contour of the paper mounted image, adaptively obtain the optimal threshold, make up for the shortcomings of the traditional Canny operator that needs to be set manually, improve the detection accuracy, and have a certain noise reduction effect. Compared with particle swarm optimization algorithm and traditional Canny algorithm, the C / a of this algorithm is reduced by 3.33% and 33.39% respectively, and the C / B is reduced by 14.94% and 54.55% respectively.