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ZHANG Jing-yuan, CHEN Bei-bei, YANG Yong-xing, ZHU Qing-sheng, LI Jin-peng, ZHAO Jin-biao. Positioning algorithm for laser spot center based on BP neural network and genetic algorithm[J]. Chinese Optics. doi: 10.37188/CO.2022-0084
Citation: ZHANG Jing-yuan, CHEN Bei-bei, YANG Yong-xing, ZHU Qing-sheng, LI Jin-peng, ZHAO Jin-biao. Positioning algorithm for laser spot center based on BP neural network and genetic algorithm[J]. Chinese Optics. doi: 10.37188/CO.2022-0084

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

doi: 10.37188/CO.2022-0084
Funds:  Supported by National Natural Science Foundation of China (No.12003067)
  • Accepted Date: 24 Aug 2022
  • Available Online: 24 Aug 2022
  • The traditional laser spot center positioning algorithm in a vibrating environment has problems such as long processing time and low accuracy. This paper proposed a laser spot center positioning method based on a genetic algorithm optimized BP neural network. This algorithm uses a BP neural network to predict the spot center position and a genetic algorithm to optimize the neural network. Based on the BP neural network, the spot center position derived by the gray weighted centroid method, centroid method, Gaussian fitting method, and the radius of laser spot obtained by the centroid method are used to predict the actual center position of the spot. Genetic algorithms are used to optimize the weights and thresholds of neural networks to improve prediction accuracy. An experimental platform is established to simulate the vibration environment by applying perturbations to the optical system and the data is collected for neural network training and algorithm verification. 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 prediction accuracy of the neural network algorithm is improved.


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