Positioning algorithm for laser spot center based on BP neural network and genetic algorithm

Aming at the problems of long processing time and low accuracy of the traditional laser spot cen-ter positioning algorithm used in a vibrating environment. We proposed a laser spot center positioning meth-od based on a genetic algorithm optimized BP neural network. A BP neural network was applied to predict the spot center position and a genetic algorithm was applied to optimize the neural network. Based on the BP neural network, the gray weighted centroid method, centroid method, Gaussian fitting method were used to obtain the spot center position, and the centroid method was used to obtain the radius of laser spot, on the above basis, we predicted the actual center position of the spot. Genetic algorithms were used to optimize the weights and thresholds of neural networks to improve prediction accuracy. An experimental platform is es-tablished to simulate the vibration environment by applying perturbations to the optical system and the data is collected to train neural network and verify the algorithm. The experimental results show that the number of calibration test iterations before and after optimization is 55 and 29, and the average errors are 0.81 pixels and 0.45 pixels, respectively. Under the optimization of the genetic algorithm, the iteration speed and predic-tion accuracy of the neural network algorithm is improved.