Abstract
Existing almost deep learning methods rely on a large amount of annotated data, so they are inappropriate for forest fire smoke detection with limited data. In this paper, a novel hybrid attention-based few-shot learning method, named Attention-Based Prototypical Network, is proposed for forest fire smoke detection. Specifically, feature extraction network, which consists of convolutional block attention module, could extract high-level and discriminative features and further decrease the false alarm rate resulting from suspected smoke areas. Moreover, we design a meta-learning module to alleviate the overfitting issue caused by limited smoke images, and the meta-learning network enables achieving effective detection via comparing the distance between the class prototype of support images and the features of query images. A series of experiments on forest fire smoke datasets and miniImageNet dataset testify that the proposed method is superior to state-of-the-art few-shot learning approaches.
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Project funding: The work was supported by the National Key R&D Program of China (Grant No. 2020YFC1511601), and Fundamental Research Funds for the Central Universities (Grant No. 2019SHFWLC01).
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Corresponding editor: Tao Xu.
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Li, T., Zhu, H., Hu, C. et al. An attention-based prototypical network for forest fire smoke few-shot detection. J. For. Res. 33, 1493–1504 (2022). https://doi.org/10.1007/s11676-022-01457-6
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DOI: https://doi.org/10.1007/s11676-022-01457-6