Deep Learning Radar Object Detection and Classification for Urban Automotive Scenarios

This paper presents a single shot detection and classification system in urban automotive scenarios with a 77 GHz frequency modulated continuous wave radar sensor. The detection and classification of road users is based on the real-time object detection system YOLO (You Only Look Once) applied to the pre-processed radar range-Doppler-angle power spectrum. To train and test the proposed system, data is gathered with a test vehicle parked on urban roads. A mean average precision of 70.64% is achieved on a separate test data set.