A Lightweight Convolution Neural Network for Automatic Disasters Recognition
Authors:
- 11 September 2022
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Abstract
This paper proposed a lightweight, efficient Convolution Neural Network model for automatic disaster recognition from aerial images. The model consists of a stack of convolutions and dense layers, and training incorporates several augmentation and data pre-processing techniques to improve the model’s generalisation. The model is evaluated on standard performance matrices like accuracy, precision, recall and the F1-score. We compared the results with state-of-the-art models, achieving a substantial boost in performance. Additionally, we trained different model variants for the quantitative analysis on publicly available datasets. With only 3 MB in size, our model is easily deployable on embedded and resource-constrained devices.Keywords
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