Abstract
There are some problems in the process of camera calibration, such as insufficient accuracy and poor accuracy. Based on the seagull algorithm, the adaptive differential evolution algorithm is combined with the seagull algorithm to optimize the multicamera calibration. The seagull algorithm can achieve good results on multiparameter problems and effectively avoid falling into local optima. In this paper, the adaptive differential search algorithm is adopted to improve the local search ability and optimize the local search and global search ability. According to Zhang Zhengyou's method, the calibrated parameter is obtained, in which the parameter is used as the initial value. Then, taking the minimum mean error as the criterion, the improved seagull algorithm (SOA-SaDE) is used to establish the objective function, and the internal parameters and distortion coefficient of the camera are further solved. Verification experiments showed that the fusion algorithm has less reprojection error and higher calibration accuracy gull algorithm.
Highlights
Multiple cameras are widely used in various fields
Experiments show that the algorithm proposed in this paper effectively reduces the error of camera reprojection; the reprojection error is reduced by 63.03%, which is 16.75% higher than the effect of the seagull algorithm, and provides a feasible method for reducing the camera reprojection error
In order to solve the local convergence problem, SaDE algorithm is not easy to fall into local convergence
Summary
Multiple cameras are widely used in various fields. Multicamera fusion can provide a wider range of real-time scene information. Erefore, improving the accuracy of camera calibration and eliminating image distortion are of great significance to the research in the deep-sea field. Xiang et al [4] proposed a calibration method based on depth learning It can be calibrated by inputting the coordinates of the original image by improving the approximation ability of the DNN network. To avoid the local optimization in the calibration calculation, this paper combines the seagull algorithm and the adaptive differential evolution algorithm and improves the seagull algorithm by absorbing the strong local searching ability of ADE, improving the accuracy and stability of camera calibration. Aiming at the problem of low calibration accuracy and obvious reprojection errors, a fusion algorithm (SOA-SaDE) is proposed based on the seagull algorithm and the adaptive differential algorithm. Experiments show that the algorithm proposed in this paper effectively reduces the error of camera reprojection; the reprojection error is reduced by 63.03%, which is 16.75% higher than the effect of the seagull algorithm, and provides a feasible method for reducing the camera reprojection error
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