Vehicle target detection from remote sensing images based on deep learning method

Wen Lou; Dudu Guo; Xin Li

doi:10.1117/12.2674676

23 May 2023 Vehicle target detection from remote sensing images based on deep learning method

Wen Lou, Dudu Guo, Xin Li

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126043U (2023) https://doi.org/10.1117/12.2674676
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

Aiming at the problem of small scale and little characteristic information of vehicles in remote sensing images, this paper proposes a multi-scale attention detection network. Firstly, VGG16 backbone network is used to obtain multi-level image features, and the cascade feature pyramid module and the dilated attention module are added to enhance the multiscale features of the target. Secondly, the focal loss function is added to the classification task to improve the fitting ability of the network to samples. Finally, the comparative experiments are conducted on the dataset. The results show that the multi-scale attention detection network has a high detection accuracy and its mAP reaches 94.56%.

Citation Download Citation

Wen Lou, Dudu Guo, and Xin Li "Vehicle target detection from remote sensing images based on deep learning method", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126043U (23 May 2023); https://doi.org/10.1117/12.2674676

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