Abstract
The Back Propagation (BP) neural network has the problems of low accuracy and poor convergence in the process of binocular camera calibration. A method based on BP neural network optimized by improved genetic simulated annealing algorithm (IGSAA-BP) is proposed to solve these problems to complete the binocular camera calibration. The method of combining Gaussian scale space and Harris corner detection operator is used for corner detection. A matched algorithm of homonymous corner is proposed by combining point-to-point spatial mapping and grid motion statistics. The pixel values of the homonymous corner and three-dimensional coordinate values are taken as the input and output of BP neural network respectively. The crossover and mutation probability of genetic simulated annealing algorithm and the annealing criterion are improved, the IGSAA-BP neural network is used to calibrate the binocular camera. The average calibration accuracy of BP neural network and IGSAA-BP neural network is 0.71mm and 0.03mm, respectively. The average calibration accuracy of binocular camera is improved by 96%. The iteration speed is increased by 20 times and global optimization ability is improved. It can be seen that the IGSAA-BP neural network can improve the calibration accuracy of binocular camera and accelerate convergence speed.
Highlights
The three-dimensional (3D) reconstruction technology based on binocular vision can restore the 3D contour information of objects in a simple and convenient way [1]–[3]
(a), traditional Speed Up Robust Feature (Surf) feature point detection algorithm is used to detect the corner of the checkerboard images. It can be seen from the effect of corner detection circled by black circle in Figure 5 (a) that part of the edges of the checkerboard images are detected as corners, and there are problems of repeated detection and low detection accuracy at the real corners of the checkerboard images
Aiming at the problem of high mismatched rate of homonymous corner, this paper proposes a matched algorithm combining point-to-point spatial mapping algorithm and grid motion statistics algorithm to solve the problem
Summary
The three-dimensional (3D) reconstruction technology based on binocular vision can restore the 3D contour information of objects in a simple and convenient way [1]–[3]. Due to the rapid development of this technology, robot navigation, visual detection, artificial intelligence and other industries have changed with significant breakthroughs. Binocular vision is different from monocular vision in that it can obtain the depth information of the object according to different positions in the object space [4]–[6]. The parallax principle is used to calculate the position deviation between the homonymous points in two images obtained from different positions so as to obtain the 3D information of the target object [7]–[9].
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