Abstract:Aiming at the Foreign Object Debris (FOD) detection of runways, this study designs a system based on intelligent vehicle-mounted 3D cameras to collect road information and detect foreign objects. This system preliminarily screens out normal roads through the difference in the distribution of the depth quantified value of the depth image, and then through the point cloud abnormal value filtering and uneven reduction algorithm to correct the parameters and reduce the amount of data, the streamlined point cloud is adapted to the road data. Improved network for foreign object detection. This network uses the X convolution in the PointCNN network to extract point cloud data for spatial features through four convolutions, which preserves the spatial information of foreign objects as much as possible and improves the detection accuracy. Through test experiments on the collected data, the method designed in this study can accurately identify foreign objects and uneven roads with an accuracy rate close to 90%.