The rapid detection of foreign object debris (FOD) is very important for the safe driving of aircraft. Considering the problems of small size FOD target recognition difficulty and poor real-time performance in traditional FOD detection technology, a FOD detection method based on voxel feature fusion is proposed. The method firstly extracts the point cloud in the region of interest and calibrates the ground point cloud according to the RANSAC method. Secondly, the voxel feature fusion algorithm is used to divide the calibrated point cloud into voxels, and the average Z value and the average reflectivity of all point clouds are fused in each voxel according to a certain proportion to form a new point. In this way, the dense point cloud is reduced in the case of protruding FOD. The ground point cloud and the point cloud of FOD on the ground are segmented. Finally, the Euclidean clustering is used based on the KD-tree algorithm and clusters the point cloud of the FOD on the ground after segmentation. The experimental results show that this method can realize FOD detection with length, width, and height greater than 1.3 cm in a distance of 30 meters. Compared with the traditional method, the detection FOD size performance increased by 35%, which provides a reference for lidar in FOD detection.