Aiming at the problem of low detection accuracy of small remote sensing image targets with small size and unobvious features in existing target detection algorithms, a small target detection algorithm based on improved yolo_v4 is proposed. The algorithm expands the scale to four during target detection, and removes the feature scale map of the minimum perception field when output; replaces the convolution layer of the feature fusion network with a hole convolution to maintain a higher resolution and a larger receptive field; The deconvolution operation performs up-sampling of high-level features, so that low-level features can learn richer semantic information. The experiment analyzed the remote sensing image with the high-voltage electric tower as the small target, and the average accuracy was increased from 90.06% to 91.47%, indicating that the algorithm can effectively improve the detection accuracy of the small target in the remote sensing image.


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    Titel :

    Small Target Detection Algorithm in Remote Sensing Image Based on Improved Yolo


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Yi (Herausgeber:in) / Martinsen, Kristian (Herausgeber:in) / Yu, Tao (Herausgeber:in) / Wang, Kesheng (Herausgeber:in) / Peng, Chaoliang (Autor:in) / Chu, Xianwu (Autor:in) / Wang, Yunming (Autor:in) / Gao, Xiue (Autor:in)

    Kongress:

    International Workshop of Advanced Manufacturing and Automation ; 2020 ; Zhanjiang, China October 12, 2020 - October 13, 2020



    Erscheinungsdatum :

    2021-01-23


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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