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UTILIZING SUPER-RESOLUTION FOR ENHANCED AUTOMOTIVE RADAR OBJECT DETECTION

DOI:
10.60864/gab6-jt61
Citation Author(s):
Asish Kumar Mishra, Kanishka Tyagi , Deepak Mishra
Submitted by:
Kanishka Tyagi
Last updated:
17 November 2023 - 12:05pm
Document Type:
Presentation Slides
Document Year:
2023
Event:
Presenters:
Asish Kumar Mishra
Paper Code:
3444
Categories:
Keywords:
 

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

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