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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    This paper proposes a SwinT-MFPN slope scene image classification model designed to balance performance, inference speed, and convergence speed, leveraging the Swin-Transformer and feature pyramid network (FPN). The proposed model overcomes the challenges associated with rapidly increasing computational complexity and slow convergence in high-resolution images. First, the Mish activation function is introduced into the FPN to construct an MFPN structure that extracts features from the original high-resolution image, producing a feature map with reduced dimensions while eliminating redundant low-level feature information to enhance key features. The Swin-Transformer, which is known for its robust deep-level feature extraction capabilities, is then employed as the model's backbone feature extraction network. The original cross-entropy loss function of the Swin-Transformer is replaced by a weighted cross-entropy loss function to mitigate the effects of imbalanced class data on model predictions. In addition, a root mean square error evaluation index for accuracy is proposed. The proposed model's stability is verified using a self-constructed dam slope dataset. Experimental results demonstrate that the proposed model achieves a mean average precision of 95.48%, with a 3.01% improvement in time performance compared to most mainstream models, emphasizing its applicability and effectiveness.

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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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