Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR  

作　　者：Fan Li Shuyao Zhang Jie Yang Zhicheng Feng Zhichao Chen 

机构地区：[1]School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou,341000,China [2]Jiangxi Provincial Key Laboratory of Maglev Technology,Ganzhou,341000,China [3]School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou,341000,China

出　　处：《Computers, Materials & Continua》2024年第9期3819-3833,共15页计算机、材料和连续体（英文）

基　　金：supported by a grant from the National Key Research and Development Project(2023YFB4302100);Key Research and Development Project of Jiangxi Province(No.20232ACE01011);Independent Deployment Project of Ganjiang Innovation Research Institute,Chinese Academy of Sciences(E255J001).

摘　　要：Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

关 键 词：Railway foreign object light detection and ranging(LiDAR) 3D object detection PointPillars parallel attention mechanism transfer learning 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]