RailVoxelDet: A Lightweight 3-D Object Detection Method for Railway Transportation Driven by Onboard LiDAR Data

3D perception in train operating environments presents significant challenges, as it must ensure both precise distance estimation and computational efficiency to meet stringent braking requirements. To date, existing 3D detection architectures, which employ dense voxel or pillar representations, encounter challenges of computational inefficiency and accuracy degradation when processing large-scale railway Light Detection And Ranging (LiDAR) data. To address this challenge, we propose RailVoxelDet, a railway-optimized 3D detector integrating the Multi-factor Dynamic Voxel Feature Encoder (MDVFE) and efficient backbone. Specifically, MDVFE converts point clouds to 2D sparse voxels, reducing computational complexity. The backbone employs residual bottlenecks with shared full connected layers and sparse convolutions, enhanced by the SimAM-Point module. Additionally, the Feature Query and Matching Module (FQMM) is proposed to establish a bottom-up multi-level feature fusion architecture. Experimental results show RailVoxelDet reaches 71.29% mAP on OSDaR23 and 61.94% mAP on AirR24, with 6.42G FLOPs and a 71.42ms inference time. It outperforms 12 comparison models, delivering state-of-the-art results.