Binocular Camera Calibration Based on BP Neural Network Optimized by Improved Particle Swarm Optimization

Binocular vision technology is widely used in daily life, and binocular camera calibration is of great significance. Aiming at the problem that the training accuracy of the current camera calibration method based on neural network is not high enough, this paper proposes a binocular camera calibration method based on the improved particle swarm optimization BP neural network (APSO-BPNN), and introduces the adaptive particle swarm optimization algorithm to optimize the BP neural network. A binocular camera calibration model is constructed, and real data are used to verify the effectiveness and feasibility of the method. The experimental results show that the dual-target calibration method based on APSO-BPNN has higher calibration accuracy than the traditional neural network calibration method.