Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1215002(2022)

Improved Tiny YOLOv4 Algorithm and Its Application in Pedestrian Detection

Yong Xuan1, Chao Han1、*, and Wenhan Sha2
Author Affiliations
  • 1Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, Anhui , China
  • 2Chery New Energy Automobile Co., Ltd., Wuhu 241000, Anhui , China
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    References(23)

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    [2] Cao S, Zhang X W, Ma J W. Trans-scale feature aggregation network for multiscale pedestrian detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 46, 1786-1796(2020).

    [3] Ju M R, Luo H B, Wang Z B et al. Improved YOLO V3 algorithm and its application in small target detection[J]. Acta Optica Sinica, 39, 0715004(2019).

    [4] Zhao B, Wang C P, Fu Q et al. Multi-scale infrared pedestrian detection based on deep attention mechanism[J]. Acta Optica Sinica, 40, 0504001(2020).

    [9] Zheng Y P, Li G Y, Li Y. Survey of application of deep learning in image recognition[J]. Computer Engineering and Applications, 55, 20-36(2019).

    [23] Adelson E H, Anderson C H, Bergen J R et al. Pyramid methods in image processing[J]. RCA Engineer, 29, 33-41(1984).

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    Yong Xuan, Chao Han, Wenhan Sha. Improved Tiny YOLOv4 Algorithm and Its Application in Pedestrian Detection[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215002

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    Paper Information

    Category: Machine Vision

    Received: Apr. 14, 2021

    Accepted: Jun. 2, 2021

    Published Online: May. 23, 2022

    The Author Email: Han Chao (hanchaozh@126.com)

    DOI:10.3788/LOP202259.1215002

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