Camera Calibration Based On Intelligent Algorithm

With the development of artificial intelligence technology in the 21 st century and the continuous improvement of energy requirements,the State Grid is facing new challenges,so it is imperative to establish an intelligent grid.In the process of building an intelligent power grid,computer vision technology can play a good role in both equipment intelligence and power grid intelligence.Whether it is to use unmanned aerial vehicle to analyze binocular images to judge distance for better inspection of power lines,or to use camera to analyze real-time images for distance measurement and control when intelligent robots work,computer vision technology is used.The camera calibration technology is an important part of computer vision technology and is the basis of many of the above work.Its accuracy directly determines the error of subsequent work,so it is of great significance to further improve the accuracy of camera calibration technology.This paper mainly studies the optimization of camera calibration parameters using intelligent algorithms and binocular vision ranging.The main work is as follows:(1)In this paper,the imaging model of the camera is established and the mathematical principle is deduced.Different from common genetic algorithm and particle swarm algorithm,bat algorithm and sine cosine algorithm are applied to the nonlinear optimization of camera calibration parameters.Physical experiments are carried out and compared with the experimental results of particle swarm optimization algorithm and Levenberg-Marquardt algorithm.Taking the average error of actual projection points and calculated projection points as the accuracy standard,the accuracy of sine and cosine algorithm is 2.34% higher than that of Levenberg-Marquardt algorithm,but the stability still needs to be further improved.(2)Looking for the rule from the optimization trend graph of camera calibration parameters,a new sine and cosine algorithm is obtained by improving conversionparameters,increasing weight coefficient and adding parallel strategy.Finally,physical experiments prove that the accuracy and stability of the improved algorithm have been further improved.The average error is 12.96% higher than that of sine and cosine algorithm,which proves the feasibility of the improved sine and cosine algorithm.(3)The experiment of binocular vision ranging is carried out in different positions,and the same feature points are taken for ranging and the mean value of error is calculated.Only different camera calibration parameter optimization methods are selected during the experiment.The experimental results prove that the improved sine cosine algorithm can reduce the experimental error when applied to binocular vision ranging.