Leveraging Self-Supervised Instance Contrastive Learning for Radar
  Object Detection
Colin Decourt Rufin VanRullen Didier Salle Thomas Oberlin
In recent years, driven by the need for safer and more autonomous transport
systems, the automotive industry has shifted toward integrating a growing
number of Advanced Driver Assistance Systems (ADAS). Among the array of sensors
employed for object recognition tasks, radar sensors have emerged as a
formidable contender due to their abilities in adverse weather conditions or
low-light scenarios and their robustness in maintaining consistent performance
across diverse environments. However, the small size of radar datasets and the
complexity of the labelling of those data limit the performance of radar object
detectors. Driven by the promising results of self-supervised learning in
computer vision, this paper presents RiCL, an instance contrastive learning
framework to pre-train radar object detectors. We propose to exploit the
detection from the radar and the temporal information to pre-train the radar
object detection model in a self-supervised way using contrastive learning. We
aim to pre-train an object detector's backbone, head and neck to learn with
fewer data. Experiments on the CARRADA and the RADDet datasets show the
effectiveness of our approach in learning generic representations of objects in
range-Doppler maps. Notably, our pre-training strategy allows us to use only
20% of the labelled data to reach a similar mAP@0.5 than a supervised approach
using the whole training set.