Laser & Optoelectronics Progress, Volume. 61, Issue 22, 2237012(2024)

High-Resolution Slope Scene Image Classification Based on SwinT-MFPN

Yin Tu1... Denghua Li2,3,* and Yong Ding1 |Show fewer author(s)
Author Affiliations
  • 1College of Science, Nanjing University of Technology, Nanjing 210094, Jiangsu , China
  • 2Nanjing Institute of Water Resources Science, Nanjing 210024, Jiangsu , China
  • 3Key Laboratory of Reservoir Dam Safety, Ministry of Water Resources, Nanjing 210024, Jiangsu , China
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    References(30)

    [2] Yu K, Jia L, Chen Y Q et al. Deep learning: yesterday, today, and tomorrow[J]. Journal of Computer Research and Development, 50, 1799-1804(2013).

    [3] Chu H H, Yuan H Q, Long L Z et al. Cascade segmentation method of high-resolution bridge crack image based on Transformer[J]. China Journal of Highway and Transport, 37, 65-76(2024).

    [17] Wang K, Ren J, Zhang W C. Few-shot image classification algorithm of graph neural network based on Swin Transformer[J]. Laser & Optoelectronics Progress, 61, 1237003(2024).

    [20] He X Y, Xu W M, Pan K X et al. Classification of high-resolution remote sensing image based on Swin Transformer and convolutional neural network[J]. Laser & Optoelectronics Progress, 61, 1628002(2024).

    [23] Zhu D L, Yu M S, Liang M F. Real-time instance segmentation of maize ears using SwinT-YOLACT[J]. Transactions of the Chinese Society of Agricultural Engineering, 39, 164-172(2023).

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    Yin Tu, Denghua Li, Yong Ding. High-Resolution Slope Scene Image Classification Based on SwinT-MFPN[J]. Laser & Optoelectronics Progress, 2024, 61(22): 2237012

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    Paper Information

    Category: Digital Image Processing

    Received: Feb. 29, 2024

    Accepted: Apr. 14, 2024

    Published Online: Nov. 19, 2024

    The Author Email: Li Denghua (dhli@nhri.cn)

    DOI:10.3788/LOP240769

    CSTR:32186.14.LOP240769

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