铁道科学与工程学报2024，Vol.21Issue(4)：1667-1676,10.DOI:10.19713/j.cnki.43-1423/u.T20231030
基于SDA-SSD的轨道交通异物检测
Rail foreign object detection based on SDA-SSD
摘要
Abstract
With the increasing scale of urban rail transportation,foreign object encroachment has become a major potential hazards for transit safety,and the foreign objects detection based on artificial intelligence methods has become a research hotspot.Compared with traditional vision camera and LiDAR methods,those methods based on artificial intelligence have the advantages of light insensitivity,high ranging accuracy,and long-distance detection,which are more suitable for safety monitoring and detection in rail transportation scenarios.In face of massive point cloud data,the existing LiDAR-based target detection methods may lead to leakage and false detection results due to the loss of 3D structure information.To address the above problem,a single-stage target detection method SDA-SSD(Structure Density Aware Single-Stage Object Detector)based on structure density awareness was proposed.The voxel feature aggregation module was designed to extract 3D structure information,and the triple feature fusion module was designed to fuse point cloud features and 3D structure information to prevent the spatial feature quality degradation caused by the extraction of high-level semantics and enhance the detection performance.The voxel density value was introduced to measure the sparsity of samples,and the classification confidence was corrected based on the voxel density value to improve the performance of inconsistent target localization and classification.The experimental results show that the proposed method achieves 88.21%average accuracy and 21 FPS detection speed in the car category of KITTI dataset,which can improve the average detection accuracy by 2.36 percentage points and detection speed by 13%compared with the benchmark network SECOND.Moreover,the proposed method was verified against the dataset in which data is collected in the actual scenario of urban rail transit.The target obstacles in front of the train can be detected successfully.Finally,the proposed method has satisfactory detection performance in the complex scenarios and can improve the safety of the rail transit effectively.关键词
智能交通/激光雷达/三维目标检测/深度学习/稀疏卷积Key words
intelligent transportation/LiDAR/3D object detection/deep learning/sparse convolution分类
交通工程引用本文复制引用
蒋伟,梁奕,肖睿,徐秋然,王志伟,曲伟强..基于SDA-SSD的轨道交通异物检测[J].铁道科学与工程学报,2024,21(4):1667-1676,10.基金项目
国家自然科学基金资助项目(61401269) （61401269）
