Autonomous vehicle, with the attributes that ensuring driving safety and improving traffic efficiency, has been a research hotspot for a long time. In the modular developing pipeline of autonomous vehicles, pedestrian detection based on computer vision is a critical component of perception module. In this paper, we apply the newly proposed network structure YOLOv5 in pedestrian detection problem. After training in PASCAL VOC2012 dataset, the model realizes high detection accuracy and real-time efficiency. At the same time, the model owns competitive generalization ability which achieve high detection accuracy in KITTI dataset. With competitive detection accuracy and real-time efficiency, YOLOv5 have the potential to be deployed on autonomous vehicles.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pedestrian Detection with YOLOv5 in Autonomous Driving Scenario


    Contributors:
    Jin, Xianjian (author) / Li, Zhiwei (author) / Yang, Hang (author)


    Publication date :

    2021-10-29


    Size :

    3890762 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    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

    Free access

    Autonomous Driving: Framework for Pedestrian Intention Estimationin a Real World Scenario

    Alvarez, Walter Morales / Moreno, Francisco Miguel / Sipele, Oscar et al. | ArXiv | 2020

    Free access

    Vehicle Recognition under Autonomous Driving Based on YOLOv5

    Zhou, Xiaozhou / Song, Hongwei / Gao, Jiaxing | IEEE | 2024