SECure: SE block-based classification of buildings post hurricane

Jinnan Zhu

doi:10.1117/12.2641032

13 October 2022 SECure: SE block-based classification of buildings post hurricane

Jinnan Zhu

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228715 (2022) https://doi.org/10.1117/12.2641032
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

When hurricane happens, immediate and accurate assessment on housing damage is critical for rescuers. It is essential for the government to detect and measure the number and rating of the damaged buildings. Traditional way is to process this task manually, which is time-consuming. Recently, with the achievement made in the field of deep learning, this task could be done more easily using Convolutional Neural Networks. In this paper, we aim to classify the damaged buildings post hurricane using CNN models. We experiment with four different models and propose the final model-- SECure. SECure utilizes SE block to generate channel attention for feature representations, leading to a better performance. SECure achieves 97.95% accuracy on validation set, which can show a significant performance on handling this task. We hope our innovation can be facilitate the usage of CNN in real-world rescue scenarios.

Citation Download Citation

Jinnan Zhu "SECure: SE block-based classification of buildings post hurricane", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228715 (13 October 2022); https://doi.org/10.1117/12.2641032

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KEYWORDS

Buildings

Data modeling

Damage detection

Image classification

Performance modeling

Machine learning

Network architectures

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