Journal of Infrared and Millimeter Waves, Volume. 41, Issue 5, 914(2022)

An ultra-efficient streaming-based FPGA accelerator for infrared target detection

Shao-Yi CHEN1,2,3,4, Xin-Yi TANG2,3,4, Jian WANG2,3,4, Jing-Si HUANG1,2,3,4, and Zheng LI2,3,4、*
Author Affiliations
  • 1School of Information Science and Technology,Shanghai Tech University,Shanghai 201210,China
  • 2Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 20083,China
  • 3University of Chinese Academy of Sciences,Beijing 100049,China
  • 4Key Laboratory of Infrared System Detection and Imaging Technology,Chinese Academy of Sciences,Shanghai 200083,China
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    References(15)

    [1] Nowosielski A, Małecki K, Forczmański P et al. Embedded Night-Vision System for Pedestrian Detection[J]. IEEE Sensors Journal, 20, 9293-9304(2020).

    [12] Li Y, Lu S, Luo J et al. 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), 335-339(2019).

    [14] Zhang X, Lu H, Hao C et al. SkyNet: a hardware-efficient method for object detection and tracking on embedded systems[J]. Proceedings of Machine Learning and Systems, 2, 216-229(2020).

    [15] Howard A G, Zhu M, Chen B et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J](2017).

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    Shao-Yi CHEN, Xin-Yi TANG, Jian WANG, Jing-Si HUANG, Zheng LI. An ultra-efficient streaming-based FPGA accelerator for infrared target detection[J]. Journal of Infrared and Millimeter Waves, 2022, 41(5): 914

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    Paper Information

    Category: Research Articles

    Received: Jan. 13, 2022

    Accepted: --

    Published Online: Feb. 6, 2023

    The Author Email: Zheng LI (lizheng_sitp@163.com)

    DOI:10.11972/j.issn.1001-9014.2022.05.016

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