  Region proposal algorithms play an important role in most state-of-the-art
two-stage object detection networks by hypothesizing object locations in the
image. Nonetheless, region proposal algorithms are known to be the bottleneck
in most two-stage object detection networks, increasing the processing time for
each image and resulting in slow networks not suitable for real-time
applications such as autonomous driving vehicles. In this paper we introduce
RRPN, a Radar-based real-time region proposal algorithm for object detection in
autonomous driving vehicles. RRPN generates object proposals by mapping Radar
detections to the image coordinate system and generating pre-defined anchor
boxes for each mapped Radar detection point. These anchor boxes are then
transformed and scaled based on the object's distance from the vehicle, to
provide more accurate proposals for the detected objects. We evaluate our
method on the newly released NuScenes dataset [1] using the Fast R-CNN object
detection network [2]. Compared to the Selective Search object proposal
algorithm [3], our model operates more than 100x faster while at the same time
achieves higher detection precision and recall. Code has been made publicly
available at https://github.com/mrnabati/RRPN .
