改进蜣螂算法优化BP网络的双目相机标定

    Binocular camera calibration using BP algorithm optimized by improved dung beetle optimizer

    • 摘要: 针对传统BP(back propagation)网络在双目相机标定过程中存在容易陷入局部最优值、标定精度较差的问题,提出了一种改进的蜣螂优化算法(improved dung beetle optimizer, IDBO)优化BP网络(IDBO-BP)来完成双目相机标定。首先介绍了基于BP网络的双目相机标定;其次采用Piecewise混沌映射初始化蜣螂种群,增加种群的多样性和随机性;然后采用随机游走策略对蜣螂滚球行为进行扰动,增加全局寻优能力;最后采用纵横交叉策略对蜣螂种群全体进行扰动,提高算法求解精度。实验仿真结果说明,BP网络和IDBO-BP网络进行双目相机标定的精度分别为0.1103 mm和0.0256 mm,标定精度提高了76.8%。使用改进后的蜣螂优化算法优化BP网络进行双目相机标定,显著提升了标定的精度。

       

      Abstract: Aiming at the problems of local minimum and poor calibration precision of traditional BP(back propagation) network in the calibration process of binocular camera, an improved dung beetle optimizer (IDBO) was proposed to optimize the BP network (IDBO-BP) for binocular camera calibration.Firstly, the binocular camera calibration based on BP network was introduced. Then the dung beetle population was initialized by Piecewise chaotic mapping, which could increase the diversity and randomness of the population. Then, the rolling behavior of dung beetle was perturbed by the random walking strategy to increase the global optimization ability. Finally, the criss-cross strategy was used to perturb the whole population to improve the accuracy of the algorithm. The experimental results show that the calibration accuracy of BP network and IDBO-BP network for binocular camera is 0.1103 mm and 0.0256 mm, respectively,the calibration accuracy is improved by 76.8% . Therefore, the binocular camera calibration accuracy could be improved by the improved dung beetle optimization algorithm optimized BP network.

       

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