提出了一种基于构建初始测量网络的相机内部参数校准方法，有效解决了二维平面靶标在深度方向信息的不足以及三维靶标空间的限制等问题。通过对靶标板进行初始成像，按照测量网络的构建原则建立初始测量网络，经过后方交会求解外方位、前方交会求解空间三维靶标点坐标，最后由光束平差优化求解相机内部参数。利用标定后内部参数求解空间点坐标，实验结果表明，采用构建初始测量网的误差平均值为0.0794，优于平面靶标和立体靶标标定的-0.2443和-0.1916。同时校准所用时间也明显小于虚拟立体校准，即该方法具有快速、精确和方便等优点，满足大空间视觉测量中相机内部参数现场校准的要求。

The camera internal parameters calibration method based on building the initial measurement of the network is put forward. The lack of direction depth of the plane target, as well as the constraints in three-dimensional target space, is solved effectively. In accordance with the principle of measurement network, after the imaging of the target board, the initial measurement network is established. After resection for solving the exterior orientation, forward intersection for the spatial coordinates of encoded points attached on the target board, the camera parameters are solved by the iterative optimization finally. And then, the internal parameters are used for solving the space coordinates. The experimental results show that an average error of building the initial network value of 0.0794 is superior to values of -0.2443 and -0.1916 of planar-taget and three-dimensional-taget calibration. Besides, the calibration time is significantly less than the virtual three-dimensional calibration. This method has the advantages of fast, accurate and efficient, can meet the requirements of the current large-scale vision measurement in the internal parameters calibration on site.