Utilizing Super-Resolution for Enhanced Automotive Radar Object Detection

In recent years, automotive radar has become an integral part of the advanced safety sensor stack. Although radar gives a significant advantage over a camera or Lidar, it suffers from poor angular resolution, unwanted noises and significant object smearing across the angular bins, making radar-based object detection challenging. We propose a novel radar-based object detection utilizing a deep learning-based super-resolution (DLSR) model. Due to the unavailability of low-high resolution radar data pair, we first simulate the data to train a DLSR model. We develop a framework that feeds a low-resolution radar dataset (called CRUW dataset) into the trained DLSR model pipeline to train a radar-based deep object detection classifier. The proposed framework achieves an 80% accuracy on object classification for the CRUW dataset and has a lower computational footprint, making it an ideal candidate for real-time implementation on edge devices used in autonomous driving applications. Code, dataset and supplementary material are on https://github.com/kanishkaisreal/DLSR_CRUW