research-article

DANet: Dimension Apart Network for Radar Object Detection

Authors:

Errui DingAuthors Info & Claims

ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval

Pages 533 - 539

https://doi.org/10.1145/3460426.3463656

Published: 01 September 2021 Publication History

Abstract

In this paper, we propose a dimension apart network (DANet) for radar object detection task. A Dimension Apart Module (DAM) is first designed to be lightweight and capable of extracting temporal-spatial information from the RAMap sequences. To fully utilize the hierarchical features from the RAMaps, we propose a multi-scale U-Net style network architecture termed DANet. Extensive experiments demonstrate that our proposed DANet achieves superior performance on the radar detection task at much less computational cost, compared to previous pioneer works. In addition to the proposed novel network, we also utilize a vast amount of data augmentation techniques. To further improve the robustness of our model, we ensemble the predicted results from a bunch of lightweight DANet variants. Finally, we achieve 82.2% on average precision and 90% on average recall of object detection performance and rank at 1st place in the ROD2021 radar detection challenge. Our code is available at: \urlhttps://github.com/jb892/ROD2021_Radar_Detection_Challenge_Baidu.

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Cited By

Jia FLi CBi SQian JWei LSun G(2024)TC–Radar: Transformer–CNN Hybrid Network for Millimeter-Wave Radar Object DetectionRemote Sensing10.3390/rs1616288116:16(2881)Online publication date: 7-Aug-2024
https://doi.org/10.3390/rs16162881
Zhuang LJiang TJiang HWang AHuang Z(2024)LQCANet: Learnable-Query-Guided Multi-Scale Fusion Network Based on Cross-Attention for Radar Semantic SegmentationIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.33422969:2(3330-3344)Online publication date: Feb-2024
https://doi.org/10.1109/TIV.2023.3342296
Decourt CVanRullen RSalle DOberlin T(2024)A Recurrent CNN for Online Object Detection on Raw Radar FramesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340407625:10(13432-13441)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3404076
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Index Terms

DANet: Dimension Apart Network for Radar Object Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

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Published In

cover image ACM Conferences

ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval

August 2021

715 pages

ISBN:9781450384636

DOI:10.1145/3460426

General Chairs:
Wen-Huang Cheng
National Yang Ming Chiao Tung University, Taiwan
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Meng Wang
Hefei University of Technology, China
,
Program Chairs:
Wei-Ta Chu
National Cheng Kung University, Taiwan
,
Jiaying Liu
Peking University, China
,
Marcel Worring
University of Amsterdam, Netherlands

Copyright © 2021 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2021

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ICMR '21

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SIGMM

ICMR '21: International Conference on Multimedia Retrieval

August 21 - 24, 2021

Taipei, Taiwan

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Jia FLi CBi SQian JWei LSun G(2024)TC–Radar: Transformer–CNN Hybrid Network for Millimeter-Wave Radar Object DetectionRemote Sensing10.3390/rs1616288116:16(2881)Online publication date: 7-Aug-2024
https://doi.org/10.3390/rs16162881
Zhuang LJiang TJiang HWang AHuang Z(2024)LQCANet: Learnable-Query-Guided Multi-Scale Fusion Network Based on Cross-Attention for Radar Semantic SegmentationIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.33422969:2(3330-3344)Online publication date: Feb-2024
https://doi.org/10.1109/TIV.2023.3342296
Decourt CVanRullen RSalle DOberlin T(2024)A Recurrent CNN for Online Object Detection on Raw Radar FramesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340407625:10(13432-13441)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3404076
Li ZJia JShi Y(2024)MonoLSS: Learnable Sample Selection For Monocular 3D Detection2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00088(1125-1135)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00088
Li YLi ZWang YXie GLin YShen WJiang W(2023)Improving the Performance of RODNet for MMW Radar Target Detection in Dense Pedestrian SceneMathematics10.3390/math1102036111:2(361)Online publication date: 10-Jan-2023
https://doi.org/10.3390/math11020361
Jiang TZhuang LAn QWang JXiao KWang A(2023)T-RODNet: Transformer for Vehicular Millimeter-Wave Radar Object DetectionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2022.322970372(1-12)Online publication date: 2023
https://doi.org/10.1109/TIM.2022.3229703
Tatarchenko MRambach K(2023)Histogram-based Deep Learning for Automotive Radar2023 IEEE Radar Conference (RadarConf23)10.1109/RadarConf2351548.2023.10149688(1-6)Online publication date: 1-May-2023
https://doi.org/10.1109/RadarConf2351548.2023.10149688
Sakthi MArvinte MVikalo H(2023)Automotive Radar Sub-Sampling via Object Detection Networks: Leveraging Prior Signal InformationIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33320434(858-869)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3332043
Shen YZhou DGao YYang X(2023)Radar Object Detection Using 3D-2D Encoder-Decoder Network2023 7th International Symposium on Computer Science and Intelligent Control (ISCSIC)10.1109/ISCSIC60498.2023.00039(146-150)Online publication date: 27-Oct-2023
https://doi.org/10.1109/ISCSIC60498.2023.00039
Zou HXie ZOu JGao Y(2023)TransRSS: Transformer-based Radar Semantic Segmentation2023 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA48891.2023.10161200(6965-6972)Online publication date: 29-May-2023
https://doi.org/10.1109/ICRA48891.2023.10161200
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