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DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps

Decourt, Colin and VanRullen, Rufin and Salle, Didier and Oberlin, Thomas DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps. (2022) In: 2022 IEEE Intelligent Vehicles Symposium (IV), 4 June 2022 - 9 June 2022 (Aachen, Germany).

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Official URL: https://doi.org/10.1109/IV51971.2022.9827281


Due to the small number of raw data automotive radar datasets and the low resolution of such radar sensors, automotive radar object detection has been little explored with deep learning models in comparison to camera and lidar- based approaches. However, radars are low-cost sensors able to accurately sense surrounding object characteristics (e.g., distance, radial velocity, direction of arrival, radar cross-section) regardless of weather conditions (e.g., rain, snow, fog). Recent open-source datasets such as CARRADA, RADDet or CRUW have opened up research on several topics ranging from object classification to object detection and segmentation. In this paper, we present DAROD, an adaptation of Faster R-CNN object detector for automotive radar on the range-Doppler spectra. We propose a light architecture for features extraction, which shows an increased performance compared to heavier vision-based backbone architectures. Our models reach respectively an mAP@0.5 of 55.83 and 46.57 on CARRADA and RADDet datasets, outperforming competing methods.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to the IEEE (Institute of Electrical and Electronics Engineers). This paper is available at : https://ieeexplore.ieee.org/document/9827281 “© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-03942527
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > NXP Semiconductors (FRANCE)
French research institutions > Artificial and Natural Intelligence Toulouse Institute - ANITI (FRANCE)
Laboratory name:
ANITI ANR-19-PI3A-0004
Deposited On:17 Jan 2023 09:24

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