We proposed a pedestrian detection algorithm combining YOLOv5 with convolution and channel attention mechanism. First, we use our own pedestrian dataset to train the YOLOv5 detection model. Then, three attention mechanisms, SE, CBAMC3, and CoordAtt, are used to enhance the detection performance. The experiment demonstrated that the precision of both SE and CoordAtt decreased, the recall of SE also decreased, while the accuracy of CBAMC3 was improved and the mAP changed little, thus CBAMC3 became the best model for pedestrian detection. Research indicates that adding convolution block attention modules can increase the precision of detecting small pedestrian targets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Research on improving pedestrian detection algorithm based on YOLOv5


    Beteiligte:
    Khan, Zeashan Hameed (Herausgeber:in) / Balas, Valentina E. (Herausgeber:in) / Lin, Xiaogang (Autor:in) / Song, Anjun (Autor:in)

    Kongress:

    International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023) ; 2023 ; Nanchang, China


    Erschienen in:

    Proc. SPIE ; 12700


    Erscheinungsdatum :

    26.05.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Pedestrian Detection with YOLOv5 in Autonomous Driving Scenario

    Jin, Xianjian / Li, Zhiwei / Yang, Hang | IEEE | 2021


    Pedestrian Detection Using YOLOv5 For Autonomous Driving Applications

    Vikram Reddy, Etikala Raja / Thale, Sushil | IEEE | 2021


    PED-AI: Pedestrian Detection for Autonomous Vehicles using YOLOv5

    Malbog Mon Arjay / Marasigan Rufo Jr. / Mindoro Jennalyn et al. | DOAJ | 2024

    Freier Zugriff