In binocular vision inspection system, the calibration of detection equipment is the basis to ensure the subsequent detection accuracy. The current calibration methods have the disadvantages of complex calculation, low precision, and poor operability. In order to solve the above problems, the calibration method of binocular camera, the correction method of lens distortion, and the calibration method of projector in the binocular vision system based on surface structured light are studied in this paper. For lens distortion correction, on the basis of analyzing the traditional correction methods, a distortion correction method based on radial basis function neural network is proposed. Using the excellent nonlinear mapping ability of RBF neural network, the distortion correction models of different lenses can be obtained quickly. It overcomes the defect that the traditional correction model cannot adjust adaptively with the type of lens. The experimental results show that the accuracy of the method can meet the requirements of system calibration.

中文翻译：

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基于神经网络的双目视觉系统标定方法研究

在双目视觉检测系统中，检测设备的校准是保证后续检测精度的基础。目前的校准方法存在计算复杂、精度低、可操作性差的缺点。为解决上述问题，本文对基于表面结构光的双目视觉系统中的双目相机标定方法、镜头畸变校正方法和投影仪标定方法进行了研究。对于镜头畸变校正，在分析传统校正方法的基础上，提出了一种基于径向基函数神经网络的畸变校正方法。利用RBF神经网络优异的非线性映射能力，可以快速得到不同镜头的畸变校正模型。克服了传统校正模型无法根据镜头类型自适应调整的缺陷。实验结果表明，该方法的准确度能够满足系统标定的要求。