Real-time Human Detection using Convolutional Neural Networks with FMCW RADAR RGB data

Abstract: Machine learning has been employed in the automotive industry together with cameras to detect objects in surround sensing technology. You Only Look Once is a state-of-the-art object detection algorithm especially suitable for real-time applications due to its speed and relatively high accuracy compared to competing methods. Recent studies have investigated whether radar data can be used as an alternative to camera data with You Only Look Once, seeing as radars are more robust to changing environments such as various weather and lighting conditions. These studies have used 3D data from radar consisting of range, angle, and velocity, transformed into a 2D image representation, either in the Range-Angle or Range-Doppler domain. Furthermore, the processed radar image can use either a Cartesian or a polar coordinate system for the rendering. This study will combine previous studies, using You Only Look Once with Range-Angle radar images and examine which coordinate system of Cartesian or polar is most optimal. Additionally, evaluating the localization and classification performance will be done using a combination of concepts and evaluation metrics. Ultimately, the conclusion is that the Cartesian coordinate system prevails with asignificant improvement compared to polar.