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

Three-dimensional 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 multiscale 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 3-D 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 3-D detection head to guide a 2-D image backbone to learn extra 3-D 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 3-D object detection network BEVDepth. Moreover, the results show that RailBEV effectively supports long-range detection and identifies multiscale and short-range objects. All these indicate that RailBEV has the potential to be readily applicable in 3-D railway object detection.