Pedestrian detection has a wide range of application scenarios in the fields of intelligent monitoring and security, but there is a problem that it cannot achieve sufficient accuracy at sufficient speed. In this context, this paper proposes an improved YOLOv3 pedestrian detection method using combined attention mechanism. Firstly, K-means clustering algorithm is used to extract the prior box of INRIA dataset, so as to obtain the prior boxes more suitable for INRIA dataset. Secondly, SE block is added to the three effective feature layers derived from the backbone Darknet53, which brings a small number of parameters, but can add weight to the features of each channel. Thus removing the side effects of ineffective or weak feature layers. The addition of SE block can effectively improve the accuracy with a small number of parameters. Finally, on the INRIA dataset, the improved YOLOv3 algorithm improves mAP by 3.15% compared with YOLOv3, and is also higher than the similar two-stage algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Improved YOLO Pedestrian Detection Algorithm Based on Attention Mechanism


    Beteiligte:
    Yao, Kun (Autor:in) / Shi, Jiefu (Autor:in) / Xia, Yu (Autor:in)


    Erscheinungsdatum :

    12.10.2022


    Format / Umfang :

    1359370 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Pedestrian Detection Based on YOLO Network Model

    Lan, Wenbo / Dang, Jianwu / Wang, Yangping et al. | British Library Conference Proceedings | 2018


    Ship detection based on improved YOLO algorithm

    Zhang, Xiaoqing / Zhang, Zhiqiang | IEEE | 2023


    Using YOLO-based pedestrian detection for monitoring UAV

    Zhang, Depei / Shao, Yanhua / Mei, Yanying et al. | SPIE | 2019



    Improved YOLO-based algorithm for urban traffic object detection

    Zhang, Liguo / Yan, Xu / Jin, Mei | SPIE | 2024