基于三维点云深度学习的路面异物检测

针对采机场跑道异物FOD(ForeignObjectDebris)检测问题,本文设计了一套基于智能车载3D相机采集路面信息并进行异物检测的系统.此系统通过深度图像的深度量化值分布差异初步筛除正常路面,再经过点云异常值过滤与不均匀降样算法对参数进行纠正和数据量缩减,精简后的点云通过对路面数据适应性改进的网络进行异物检测.此网络采用PointCNN网络中的X卷积通过4次卷积提取点云数据进行空间特征,尽可能的保留了异物目标的空间信息,提高检测准确度.通过对采集的数据进行测试实验,本文设计的方法能够准确地识别出异物与非平整路面,准确率接近90％.

Foreign Object Debris Detection of Pavement Based on Deep Learning of 3D Point Cloud

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%.