In the twenty-first century, traffic problems are still troubling people. The concept of intelligent transportation was proposed to solve this problem. The main problem in intelligent traffic is to obtain information about the vehicles on the road and use this information to manage traffic problems. Most of the data now comes from the driveway monitor. The camera cannot focus on everywhere. Therefore, when some accident occurs, UAV can collect information as fast as possible. UAV is more inflexible than a fixed camera. In terms of real time, YOLOv3 is the best choice, because YOLOv3 is extremely fast. Thus, this study has important significance in promoting the development of intelligent transportation.


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    Titel :

    Vehicle Detection in Aerial Images Based on YOLOv3


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Liang, Qilian (Herausgeber:in) / Wang, Wei (Herausgeber:in) / Mu, Jiasong (Herausgeber:in) / Liu, Xin (Herausgeber:in) / Na, Zhenyu (Herausgeber:in) / Cai, Xiantao (Herausgeber:in) / Hu, Ruiheng (Autor:in) / Chen, Bingcai (Autor:in) / Tang, Tiantian (Autor:in)


    Erscheinungsdatum :

    2021-02-09


    Format / Umfang :

    6 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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