Chinese Journal of Liquid Crystals and Displays, Volume. 40, Issue 6, 931(2025)

Multi-scale pedestrian detection algorithm based on joint head and overall information

Ximing MA1, Ning LI1、*, Di WU1, Jianfei WANG2, and Xiangyue YU1
Author Affiliations
  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
  • 2Military Representative Office Stationed in a Region by Air Force Equipment Department, China
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    References(38)

    [3] LI X Y, FU H T, NIU W T et al. Multi-modal pedestrian detection algorithm based on deep learning[J]. Journal of Xi'an Jiaotong University, 56, 61-70(2022).

    [9] CHU J, SHU W, ZHOU Z B et al. Combining semantics with multi-level feature fusion for pedestrian detection[J]. Acta Automatica Sinica, 48, 282-291(2022).

    [13] SHI R J, CHEN H J, LI J P et al. Small-scale pedestrian detection algorithm based on attention and multi-level feature fusion for railway[J]. Journal of the China Railway Society, 44, 76-83(2022).

    [21] ZHANG K, XIONG F, SUN P Z et al. Double anchor R-CNN for human detection in a crowd[J/OL](2019).

    [22] SHAO S, ZHAO Z J, LI B X et al. CrowdHuman: A benchmark for detecting human in a crowd[J/OL](2018).

    [28] GE Z, LIU S T, WANG F et al. YOLOX: exceeding YOLO series in 2021[J/OL](2021).

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    Ximing MA, Ning LI, Di WU, Jianfei WANG, Xiangyue YU. Multi-scale pedestrian detection algorithm based on joint head and overall information[J]. Chinese Journal of Liquid Crystals and Displays, 2025, 40(6): 931

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    Paper Information

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    Received: Dec. 23, 2024

    Accepted: --

    Published Online: Jul. 14, 2025

    The Author Email: Ning LI (lining@ciomp.ac.cn)

    DOI:10.37188/CJLCD.2024-0352

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