基于BP神经网络的桥梁施工线形相机测量标定

Camera Calibration of Bridge Alignment Measurement Based on BP Neural Network

机器视觉位移测量技术为大跨桥梁线形控制提供新解,而确保高精度的二维到三维坐标转换至关重要。对此,提出一种基于改进遗传算法BP神经网络的提升双目相机标定精度的方法,通过改进传统神经网络中的交叉及变异概率函数,提高标定效率及准确性。经相应试验算例验证,采取传统张氏标定法测量坐标的均方差误差为4.67 mm,应用该方法标定后测量坐标的均方差误差为0.82 mm,标定精度提高,能够满足桥梁施工线形的监控要求。

Machine vision displacement measurement technology provides a new solution for linear control of large-span bridges,and ensuring high-precision two-dimensional to three-dimensional coordinate conversion is crucial.A method based on improved genetic algorithm BP neural network is proposed to improve the calibration accuracy of binocular cameras.By improving the crossover and mutation probability functions in traditional neural networks,the calibration efficiency and accuracy are improved.Through corresponding experimental examples,it has been verified that the mean square error of measuring coordinates using the traditional Zhang calibration method is 4.67 mm.After applying this method for calibration,the mean square error of measuring coordinates is 0.82 mm,which improves the calibration accuracy and can meet the monitoring requirements of bridge construction linearity.