SSRaDNet: An Empirical Study of Radar Object Detection Evaluation and Deployment Trade-Offs

Millimeter-wave radar is an attractive sensing modality for autonomous driving because of its robustness under adverse weather and lighting conditions. However, comparisons between radar object detectors are hindered by inconsistent evaluation procedures and limited reporting of deployment costs. This work presents a comprehensive benchmark using standardized VOC mean average precision (mAP) and evaluates processing-pipeline latency across preprocessing, network inference, and post-processing. We evaluate SSRaDNet, a lightweight hybrid CNN–Swin Transformer detector, across analog-to-digital converter (ADC), range-Doppler (RD), and range-azimuth-Doppler (RAD) representations and compare it with existing methods on RADIal and RADDet. SSRaDNet achieves state-of-the-art AP@0.5 among re-evaluated RADIal models using RD input, reaching 88.89%, and provides the strongest overall balance of detection and segmentation performance among the compared ADC configurations. On RADDet, it achieves the best performance among methods using the native-resolution RAD tensor (45.69% mAP@0.5) and introduces the first ADC-based detector with full bounding-box regression. We also evaluate alternative post-processing strategies, including hybrid MaxPoolNMS–GreedyNMS, to characterize accuracy–latency trade-offs associated with alternative post-processing strategies. These results demonstrate the importance of standardized evaluation and full-pipeline runtime analysis for radar-based object detection.