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