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In order to solve the problems of low accuracy and efficiency in manual inspection of aircraft engine intake ducts, a foreign object detection method based on curvature mutation of three-dimensional point cloud is proposed for intake ducts. Firstly, the spatial shape variation of the inlet point cloud information is analyzed for the invasion of foreign objects. Secondly, a voxel network-based point cloud data compression method is proposed to divide the point cloud into voxel grids based on the horizontal and vertical field angles of depth camera. The point cloud in each voxel grid is averaged to compress data. Thirdly, the curvature variation of point cloud data is calculated in the depth direction of the inlet. The iterative threshold method is used to calculate the dynamic threshold to recognize the point cloud of curvature mutation from foreign objects. The region growth method is used to cluster the discrete point cloud of foreign object into the whole foreign object, thereby detecting and locating foreign objects in the inlet. Finally, an analog test environment is constructed for the intake duct of aircraft engine, in which three different sizes of foreign objects are detected and located. The recognition rate and the locating accuracy for foreign object reache 95.4% and 1.46cm, respectively. The experimental results verify the accuracy and efficiency of the method proposed in this paper.