Cross-View and Cross-Modal Contrastive Learning for Radar Object Detection

Frequency-modulated continuous-wave radar is a cornerstone of advanced driver assistance systems thanks to its low cost and resilience to adverse weather. Yet the absence of explicit semantics makes radar annotation difficult, and the scarcity of large-scale labeled data limits the performance of radar perception models. To address this issue, we propose a self-supervised framework for object detection directly from Range–Azimuth– Doppler (RAD) cubes that learns transferable representations from unlabeled radar data. Specifically, we introduce cross-view contrastive learning to model correspondences among complementary views of the RAD cube, encouraging the network to capture spatial structure from multiple perspectives. In addition, an auxiliary cross-modal contrastive objective distills semantic knowledge from vision into radar. The joint objective integrates cross-view and cross-modal signals to strengthen radar feature representations. We further extend the framework to cross-domain pretraining using datasets from different sources. Experimental results demonstrate that the proposed method significantly improves radar object detection performance, especially with limited labeled data.