Volume 16Issue 2
Mar. 2023
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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, 2023, 16(2): 407-414. 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, 2023, 16(2): 407-414.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)
  • Received Date:28 Apr 2022
  • Accepted Date:24 Aug 2022
  • Rev Recd Date:09 May 2022
  • Available Online:24 Aug 2022
  • Aming at the problems of long processing time and low accuracy of the traditional laser spot center positioning algorithm used in a vibrating environment. We proposed a laser spot center positioning method 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 established 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 prediction accuracy of the neural network algorithm is improved.

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