The traditional way of river sewage outlets management highly relies on artificial inspection, which is time-consuming and labor-consuming, with limited covered areas. The drawbacks still exist when UAV inspection is now using for sewage outlets management, due to the lack of ability that automatically recognizes the river sewage outlets on UAV aerial images. To solve this problem, this research studies the sewage outlet recognition method based on deep learning. After the image preparation work such as image preprocessing, registration, and mosaic, a YOLOv3 model is selected among various common models, and adjusted to improve model performance. The research help management departments improve their working efficiency on river sewage outlets management, and also make a solid foundation for a long-term mechanism for the management.


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    Titel :

    Recognition Method of River Sewage Outlets in UAV Aerial Images Based on Deep Learning


    Weitere Titelangaben:

    Lecture Notes on Data Engineering and Communications Technologies


    Beteiligte:
    Atiquzzaman, Mohammed (Herausgeber:in) / Yen, Neil (Herausgeber:in) / Xu, Zheng (Herausgeber:in) / Wu, Shuang (Autor:in) / Yang, Qifan (Autor:in) / Ye, Jiasheng (Autor:in) / Wang, Xiaocong (Autor:in) / Huang, Shuyu (Autor:in) / Gu, Tao (Autor:in) / Cai, Shiting (Autor:in)

    Kongress:

    International conference on Big Data Analytics for Cyber-Physical-Systems ; 2021 ; Shanghai, China December 28, 2021 - December 29, 2021



    Erscheinungsdatum :

    10.12.2021


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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