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 [email protected] than a supervised approach using the whole training set.

翻译：近年来，受更安全、更自主交通系统需求的推动，汽车行业已转向集成越来越多的先进驾驶辅助系统。在用于目标识别任务的各类传感器中，雷达传感器因其在恶劣天气或低光照条件下的能力以及在多种环境中保持稳定性能的鲁棒性，已成为极具竞争力的选择。然而，雷达数据集规模较小以及数据标注的复杂性限制了雷达目标检测器的性能。受自监督学习在计算机视觉领域取得显著成果的启发，本文提出RiCL——一种用于预训练雷达目标检测器的实例对比学习框架。我们提议利用雷达检测结果和时序信息，以自监督方式通过对比学习预训练雷达目标检测模型。旨在预训练目标检测器的骨干网络、特征头及特征颈，从而用更少的数据进行学习。在CARRADA和RADDet数据集上的实验表明，我们的方法在距离-多普勒图中学习目标通用表征的有效性。值得注意的是，我们的预训练策略仅需使用20%的标注数据即可达到与使用完整训练集的监督方法相当的[email protected]性能。