Binocular camera calibration using a BP algorithm optimized by an improved subtraction-average-based optimizer

The BP algorithm exhibits drawbacks such as reduced precision and extended iteration duration during the process of calibrating a binocular camera system. A technique employing an improved subtraction-average-based optimizer to optimize the BP algorithm (ISABO-BP) is introduced to address these limitations and complete the calibration of the binocular camera. The pixel values of the corner in 2D space and the coordinate values in 3D space are used as the input and output for the BP algorithm, respectively. First, the logistic chaotic mapping is used to initialize the population to increase the diversity of the initial population. The piecewise mapping is employed to generate random values to replace the coefficient values in the individual position update formula, making the population distribution more uniform. The golden sine strategy is adopted to help particles escape from local optima. The ISABO-BP algorithm is utilized to perform the process of calibrating a binocular camera. Then, through numerical experiments, the mean calibration precision for the BP algorithm is 0.1501 and 0.0445 cm for the ISABO-BP algorithm. The calibration precision has been enhanced by 70.4%. The iterative steps of the BP algorithm are 353 epochs, while the ISABO-BP algorithm are 76 epochs, and the iterative speed is increased by nearly four times. It is evident that the ISABO-BP algorithm enhances the calibration precision and expedites the convergence rate. Finally, other intelligent algorithms to optimize the BP algorithm for calibrating a binocular camera are compared, and the superiority of this algorithm is also verified.