  This paper presents two variations of architecture referred to as RANet and
BIRANet. The proposed architecture aims to use radar signal data along with RGB
camera images to form a robust detection network that works efficiently, even
in variable lighting and weather conditions such as rain, dust, fog, and
others. First, radar information is fused in the feature extractor network.
Second, radar points are used to generate guided anchors. Third, a method is
proposed to improve region proposal network targets. BIRANet yields 72.3/75.3%
average AP/AR on the NuScenes dataset, which is better than the performance of
our base network Faster-RCNN with Feature pyramid network(FFPN). RANet gives
69.6/71.9% average AP/AR on the same dataset, which is reasonably acceptable
performance. Also, both BIRANet and RANet are evaluated to be robust towards
the noise.
