RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation

Oded Bialer, Yuval Haitman; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15407-15416

Object detection in radar imagery with neural networks shows great potential for improving autonomous driving. However obtaining annotated datasets from real radar images crucial for training these networks is challenging especially in scenarios with long-range detection and adverse weather and lighting conditions where radar performance excels. To address this challenge we present RadSimReal an innovative physical radar simulation capable of generating synthetic radar images with accompanying annotations for various radar types and environmental conditions all without the need for real data collection. Remarkably our findings demonstrate that training object detection models on RadSimReal data and subsequently evaluating them on real-world data produce performance levels comparable to models trained and tested on real data from the same dataset and even achieves better performance when testing across different real datasets. RadSimReal offers advantages over other physical radar simulations that it does not necessitate knowledge of the radar design details which are often not disclosed by radar suppliers and has faster run-time. This innovative tool has the potential to advance the development of computer vision algorithms for radar-based autonomous driving applications.