Vehicle pedestrian detection is a key aspect in driver assistance systems, which need to accurately detect all vehicle pedestrian targets on the roadway in order to ensure driving safety. To solve the problem of low accuracy in vehicle pedestrian target detection, this paper proposes a vehicle pedestrian detection method based on the improved YOLOv5 algorithm. In this paper, the initial anchor boxes of the dataset are re-clustered by the K-means clustering algorithm, and the CIOU loss function and DIOU_nms, are applied to the YOLOv5 algorithm to improve the target recognition effect and reduce the false and missed detection rate of small targets. The experimental results show that the mAP@0.5 of the improved YOLOv5 algorithm is improved by 1.85%.


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    Titel :

    Vehicle pedestrian detection method based on improved YOLOv5 algorithm


    Beteiligte:
    Wang, Gang (Herausgeber:in) / Chen, Lei (Herausgeber:in) / Chen, Zhao-hui (Autor:in) / Ling, Xiao-ming (Autor:in)

    Kongress:

    Third International Conference on Signal Image Processing and Communication (ICSIPC 2023) ; 2023 ; Kunming, China


    Erschienen in:

    Proc. SPIE ; 12916


    Erscheinungsdatum :

    20.10.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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