CNN-Swin Backbones in Radar Object Detection for Autonomous Vehicles using Raw ADC Signals

Radar-based object detection has become a popular research topic in the past few years because of the resilience of radar sensors in adverse lighting and weather conditions compared to traditional camera and LiDAR systems. Most radar deep learning models utilize a processed form of the radar signal as it is easier to extract essential information from these forms. However, these processing steps can significantly increase the inference time and ultimately limit the ability to use radar object detection in a real-time environment on autonomous vehicles. Recent research has been able to utilize raw radar signals to maintain fast inference speeds, but this has come at the cost of performance. We build on this research by first introducing the Separable Convolution (SCON) block, a lightweight CNN-based block to extract local features. The SCON block is combined with the global feature extraction capabilities of Swin Transformers to create a highly efficient backbone. This hybrid CNN-Swin backbone was used for object detection and classification on the RADDet dataset and achieved a mAP of 55.6%, which is state-of-the-art for raw radar input, and achieves state-of-the-art parity for processed radar inputs. These results were achieved while also maintaining a fast inference time.