  An efficient and accurate traffic monitoring system often takes advantages of
multi-sensor detection to ensure the safety of urban traffic, promoting the
accuracy and robustness of target detection and tracking. A method for target
detection using Radar-Vision Fusion Path Aggregation Fully Convolutional
One-Stage Network (RV-PAFCOS) is proposed in this paper, which is extended from
Fully Convolutional One-Stage Network (FCOS) by introducing the modules of
radar image processing branches, radar-vision fusion and path aggregation. The
radar image processing branch mainly focuses on the image modeling based on the
spatiotemporal calibration of millimeter-wave (mmw) radar and cameras, taking
the conversion of radar point clouds to radar images. The fusion module
extracts features of radar and optical images based on the principle of spatial
attention stitching criterion. The path aggregation module enhances the reuse
of feature layers, combining the positional information of shallow feature maps
with deep semantic information, to obtain better detection performance for both
large and small targets. Through the experimental analysis, the method proposed
in this paper can effectively fuse the mmw radar and vision perceptions,
showing good performance in traffic target detection.
