Weather recognition using DenseNet with multihead attention mechanism

Xian Zhang; Shaoxin Yuan; Qiluo Chao; Haichao Shen

doi:10.1117/12.3045382

9 October 2024 Weather recognition using DenseNet with multihead attention mechanism

Xian Zhang, Shaoxin Yuan, Qiluo Chao, Haichao Shen

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 132881P (2024) https://doi.org/10.1117/12.3045382
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

In the field of weather image recognition, the recognition of weather categories should not only consider local features, but also needs to consider the global multi-feature synthesis to avoid misjudge. To address this issue, a weather recognition method based on image organic fusion of deep neural network DenseNet and multi-head attention mechanism is proposed. Among them, DenseNet is responsible for capturing local features in weather images, while the attention mechanism is responsible for focusing on the global feature information of the image, so that the organic fusion of local and global features can improve the accuracy of weather recognition. The proposed method can recognize six types of weather—cloudy, haze, rainy, snow, sunny and thunder. The experimental results show that the model using DenseNet alone achieves 85.82% accuracy on the test set, while the accuracy of the model with the fusion of the attention mechanism increases to 87.45%. This suggests that fusing deep neural networks with attention mechanisms is an effective way to recognize weather categories.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xian Zhang, Shaoxin Yuan, Qiluo Chao, and Haichao Shen "Weather recognition using DenseNet with multihead attention mechanism", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 132881P (9 October 2024); https://doi.org/10.1117/12.3045382

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KEYWORDS

Feature extraction

Image processing

Data modeling

Education and training

Image fusion

Machine learning

Rain

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