Image Principal Distance Calibration Method by Binocular Camera Based on Improved Differential Evolution Algorithm

The traditional camera calibration method usually relies on the calibration plate, mainly uses the exhaustive method, and converts the spatial coordinates into image plane coordinates by solving the resection of the equation. The accuracy and calculation rate of traditional algorithms can no longer fully meet the needs of engineering applications. In view of this situation, this paper proposes an improved differential evolution algorithm (IDE) method for binocular camera calibration. The differential evolution algorithm (DE) is an algorithm that simulates the biological evolution process and uses the survival of the fittest to gradually screen individuals to find the optimal solution. The standard DE algorithm needs to perform population initialization, mutation operation and crossover operation, and finally screen individuals through greedy algorithm. In the mutation operation of the DE algorithm, the coefficient of variation F plays a crucial role in the result but it is difficult to obtain a value. Therefore, it is necessary to optimize the operator of the coefficient of variation F, so that the coefficient of variation F also reaches the optimal solution, which is the IDE algorithm. In this paper, the traditional calculation method, the standard DE algorithm and the IDE algorithm are used to calibrate the principal distance of the two images, and the accuracy and iteration rate are compared. The experimental results show that the IDE algorithm has obvious advantages in terms of accuracy, especially the calculation speed.