针对复杂工件自动化视觉检测中存在的视点冗余和双目成像中遮挡影响视点质量的问题，提出了一种基于表面曲率的双目视觉检测规划方法。首先根据扫描件网格模型的曲率分布和网格最大曲率方向，优化采样视点数量和姿态，提出改进的两步筛选贪婪算法用于筛选网格不均匀条件下的最优视点集，通过模拟退火算法优化视点访问顺序，生成相机无碰撞条件下的最短视觉检测路径。经仿真和实机验证，该方法相较传统检测规划方法，在满足表面覆盖率要求的前提下，视点数量减少37%，检测路径缩短26.56%，在自动化视觉检测场景下有效提升了扫描效率和精度。

To address the problems of viewpoint redundancy in the automated visual inspection of complex workpieces and degraded viewpoint quality induced by occlusion in binocular imaging, a binocular vision inspection planning method based on surface curvature is proposed. First, the number and pose of sampling viewpoints are optimized according to the curvature distribution and the direction of maximum curvature of the mesh model of the scanned part. An improved two-step screening greedy algorithm is developed to select the optimal viewpoint set under the condition of uneven meshing, and the simulated annealing algorithm is adopted to optimize the viewpoint visiting sequence, thus generating the shortest collision-free visual inspection path for the camera. Verified by both simulation and physical experiments, compared with traditional inspection planning methods, the proposed method reduces the number of viewpoints by 37% and shortens the inspection path by 26.56% while satisfying the surface coverage requirement, which effectively improves the scanning efficiency and accuracy in automated visual inspection scenarios.