计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 221100170-5.doi: 10.11896/jsjkx.221100170

• 图像处理&多媒体技术 • 上一篇    下一篇

基于GA-BP的圆形靶标圆心定位误差预测建模与补偿研究

陈海燕1, 朱军林1, 王平2   

  1. 1 兰州理工大学计算机与通信学院 兰州 730050
    2 兰州理工大学电气工程与信息工程学院 兰州 730050
  • 发布日期:2023-11-09
  • 通讯作者: 陈海燕(chenhaiyan@sina.com)
  • 基金资助:
    国家自然科学基金(62161019,62001198,62073161);甘肃省青年科技基金计划(20JR10RA186)

Study on Prediction Modeling and Compensation of Circular Target Center Positioning Error Based on GA-BP

CHEN Haiyan1, ZHU Junlin1, WANG Ping2   

  1. 1 School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China
    2 School of Electrical Engineering and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China
  • Published:2023-11-09
  • About author:CHEN Haiyan,born in 1978,Ph.D,associate professor.Her main research interests include computer vision and small target detection.
  • Supported by:
    National Natural Science Foundation of China(62161019,62001198,62073161) and Gansu Youth Science and Technology Fund program(20JR10RA186).

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摘要： 利用圆形靶标进行相机标定时,靶标成像效果会随着不同的相机拍摄位姿呈现为椭圆,因此利用常规圆心定位方法得到的图像圆心坐标并非真实圆心在图像中的成像位置,直接利用该圆心图像坐标进行相机标定的标定精度不高。针对此问题,提出了一种先对圆形靶标图像圆心定位误差进行预测建模,然后进行误差补偿来提高圆心定位精度的方法。首先,建立圆形靶标成像图的仿真图像集;其次,对图像预处理并利用椭圆拟合法定位图像中的圆心坐标;再次,构建并训练GA-BP神经网络,建立圆心定位误差与相机镜头位姿之间的关系模型;最后,通过误差补偿策略对定位的圆心坐标进行误差补偿。实验结果表明,所构建的GA-BP神经网络模型对圆心定位的横、纵坐标的误差预测精度明显优于BP或者E-R模型,其MAPE,RMSE,R2分别为5.51%,0.004 8,0.999 6和6.14%,0.096 4,0.999 8。误差补偿后的圆心定位精度更高,验证了采用误差预测建模和误差补偿的方法提高圆心定位精度的可行性,为高精度相机标定任务提供了方法支撑。

关键词: 圆形靶标, 椭圆拟合, 圆心定位误差, 预测建模, BP神经网络, 遗传算法, 误差补偿

Abstract: When using circular target for camera calibration,the target imaging effect will be elliptical with different camera shooting positions,so the image circle center coordinates obtained by using the conventional circle center positioning method are not the real circle center imaging position in the image,and the calibration accuracy is not high when using the circle center image coordinates for camera calibration directly.To address this problem,a method is proposed to model the error prediction of circular target image circular center positioning error,and then carry out error compensation to improve the circular center positioning accuracy.Firstly,a simulated image set of circular target image is established.Secondly,the image is pre-processed and the ellipse fitting method is used to locate the circle center coordinates in the image.Thirdly,a GA-BP neural network is constructed and trained to establish the relationship model between the circle center localization error and the camera lens position.Finally,the error compensation strategy is used to compensate for the localized circle center coordinates.Experimental results show that the error prediction accuracy of the constructed GA-BP neural network model for the horizontal and vertical coordinates of the circular center positioning is significantly better than that of the BP or E-R models,with MAPE,RMSE,and R2 of 5.51%,0.004 8,0.999 6and 6.14%,0.096 4,0.999 8,respectively.The accuracy of the circular center positioning after error compensation is higher,which verifies the feasibility of using error prediction modeling and error compensation to improve the accuracy of circular center positioning,and provides method support for the high-precision camera calibration task.

Key words: Circular target, Ellipse fitting, Circular center positioning error, Predictive modeling, BP neural network, Genetic algorithm, Error compensation

中图分类号: 

  • TP391
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