针对大尺寸平面零件尺寸测量系统标定精度不高的问题，提出了一种基于深度信息的系统标定方法。首先利用圆形平面靶标，提出一种提取靶标图像特征点的新方法，采用自适应阈值的边缘检测和多项式拟合算法提取特征点亚像素轮廓，利用椭圆拟合得到中心坐标；然后根据带有畸变的非线性成像几何模型，采用最小二乘法计算摄像机参数的最优解，获得靶标的位姿；最后提出被测物表面与靶标平面之间的深度信息作为摄像机模型修正项，校正测量平面位姿，利用成像原理和直线与零件表面交点确定零件尺寸。设计了单目视觉尺寸测量系统并进行实验，结果表明：标定反投影误差小于0.02 pixel，在1×0.75 m²的视场内，系统测量精度达到了0.05 mm。

A new algorithm of system calibration based on depth constraint was presented to improve the calibration accuracy of large scale planar part vision measurement system. Firstly, an algorithm of extracting the round marks of calibration plate was proposed. Sub-pixel contours of marks were extracted by edge detection algorithm based on adaptive threshold and polynomial fitting, then the center coordinates can be computed by ellipse fitting method. Then, on the basis of analyzing camera distortion model, the optimal solution of internal parameters and plate poses were obtained using least square method. Finally, the pose of measuring plane was transformed by depth information as a correction term between parts surface and calibration plate. The dimensions could be calculated using imaging principle and the intersection point of the line and the part surface. Measurement system was designed and experiments were carried out. The result shows that the calibration back-projection error is less than 0.02 pixel and the precision of the system is up to 0.05 mm in the range of 1×0.75 m².