The target detection algorithm is mainly affected by multiple factors such as illumination, clarity, overlap, target size, detection accuracy, and speed decrease greatly in adverse weather conditions. In order to solve these problems, we test the detection performance of YOLOv3 for pedestrians and vehicles with fogged photos. The fogging method is based on the airlight model. The results are shown that the YOLOv3 algorithm can be used for pedestrian and vehicle detection in haze environments, which may be a useful guideline for developing and improving related traffic safety detection systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Detection Based on YOLOv3 in Adverse Weather Conditions


    Contributors:


    Publication date :

    2022-10-12


    Size :

    1689336 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle detection method based on improved YOLOv3

    Qi, Cheng / Shen, Xizhong | IEEE | 2022


    Vehicle Detection Based on Improved Yolov3 Algorithm

    Zhao, Shuai / You, Fucheng | IEEE | 2020


    Vehicle Detection Method Based on ADE-YOLOV3 Algorithm

    Yunxiang Liu, Guoqing Zhang, Yuanyuan Zhang: SIT, Shanghai | BASE | 2020

    Free access

    Improving Performance of YOLOv3 for Vehicle Detection

    Prihatmaja, Pratamamia A. / Widyantoro, Dwi H. | IEEE | 2019


    Vehicle Detection in Aerial Images Based on YOLOv3

    Hu, Ruiheng / Chen, Bingcai / Tang, Tiantian | Springer Verlag | 2021