Object detection is to accurately identify and locate the target in the image. With the development of deep learning, object detection algorithm has achieved very high achievements, for example, object detection algorithm is applied to security, medical, transportation, military, automatic driving and so on. This paper reviews the progress of target detection algorithms by consulting many literatures at home and abroad. Firstly, the research status of object detection is introduced, and the dataset, evaluation index and convolutional neural network used in the process of target detection are provided. Secondly, it compares some mainstream object detection algorithms at this stage. The starting point of this paper is to help others quickly understand the target detection algorithm. Readers can find the appropriate object detection algorithm according to their own project and improve it on this basis. Finally, combined with the current research hotspots, the future development trend is analyzed.


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    Titel :

    A Survey of Deep Learning Object Detection Algorithm


    Beteiligte:
    Guo, Junmei (Autor:in) / Lou, Haitong (Autor:in) / Chen, Haonan (Autor:in) / Duan, Xuehu (Autor:in) / Liu, Haiying (Autor:in)


    Erscheinungsdatum :

    12.10.2022


    Format / Umfang :

    1280465 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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