RailBEV: A Vision-Based Bird’s-Eye-View Representation Network for 3-D Railway Object Detection

3D railway object detection is vital to the environmental perception of intelligent railway transportation systems. However, owing to the complexity of railway environments, critical issues such as the detection of multi-scale and distant objects remain to be overcome. This study proposes a vision-based architecture called RailBEV, which introduces bird’s-eye-view (BEV) representation within 3D railway object detection. RailBEV is mainly divided into two parallel parts: the Depth Supervision View Transformation (DSVT) and Long Range Supervision (LRS) modules. Specifically, the DSVT module designed a novel depth supervision method to transform the front view (FV) images into the BEV plane. The LRS module leverages an additional 3D detection head to guide a 2D image backbone to learn extra 3D object information and enhance the capability of long-range object detection. Experimental results demonstrate that the proposed RailBEV exhibits a mean Average Precision (mAP) of 38.6%, which is 4.4% higher than the baseline 3D object detection network BEVDepth. Moreover, the results show that RailBEV effectively supports long-range detection and identifies multi-scale and short-range objects. All these indicate that RailBEV has the potential to be readily applicable in 3D railway object detection.