Camera calibration method based on integrated dynamic dispersion-enhanced particle swarm optimization algorithm

A camera calibration method based on an integrated dynamic dispersion-enhanced particle swarm optimization (IDDE-PSO) algorithm, comprising the following steps:

• S1, acquiring a plurality of images of a calibration board of different angles, converting the plurality of images of the calibration board into grayscale images, detecting Harris corner points of the grayscale images, and solving sub-pixel coordinates;
• S2, estimating, by using the sub-pixel coordinates and a distortion camera model, initial values X0 of camera intrinsic parameters through Zhang's camera calibration method;
• S3, initializing parameters of the IDDE-PSO algorithm, calibrating the camera intrinsic parameters based on the initial values of the camera intrinsic parameters, and calculating fitness values of particles;
• S4, determining whether the fitness values of the particles have reached a convergence condition;
• when the fitness values of the particles have not reached the convergence condition, performing adaptive nonlinear adjustment on an inertial parameter, adaptively adjusting learning factors by using sine and cosine variations, iteratively updating velocities and positions of the particles, continuously updating individual optimal positions and global optimal positions of the particles by using a greedy selection algorithm, optimizing the camera intrinsic parameters by using the IDDE-PSO algorithm, and determining whether a maximum number of iterations is reached or a specific fitness threshold is met;
• S5, determining whether the IDDE-PSO algorithm is trapped in a local optimum when the fitness values of the particles in step S4 have reached the convergence condition;
• when the IDDE-PSO algorithm is trapped in the local optimum, introducing a Cauchy perturbation-based particle swarm optimization algorithm, updating the individual optimal positions of the particles and re-evaluating the fitness values of the particles, continuously updating the individual optimal positions and the global optimal positions of the particles by using the greedy selection algorithm, optimizing the camera intrinsic parameters by using the IDDE-PSO algorithm, and determining whether the maximum number of iterations is reached or the specific fitness threshold is met;
• when the IDDE-PSO algorithm is not trapped in the local optimum, directly determining whether the maximum number of iterations is reached or the specific fitness threshold is met;
• and
• S6, outputting camera parameters corresponding to a global optimal solution of the particles, when the maximum number of iterations is reached or the specific fitness threshold is met;
• returning to execute steps S3 to S5 until the maximum number of iterations is reached or the specific fitness threshold is met, when the maximum number of iterations is not reached and the specific fitness threshold is not met.