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Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

Samarth, G.; Bhowmik, N.; Breckon, T.P.

Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection Thumbnail


Authors

G. Samarth

N. Bhowmik



Contributors

M. Arif Wani
Editor

Taghi M. Khoshgoftaar
Editor

Dingding Wang
Editor

Huanjing Wang
Editor

Naeem (Jim) Seliya
Editor

Abstract

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced complexity deep CNN architectures for this task and evaluate the effects of different optimization and normalization techniques applied to different CNN architectures (spanning the Inception, ResNet and EfficientNet architectural concepts). Contrary to contemporary trends in the field, our work illustrates a maximum overall accuracy of 0.96 for full frame binary fire detection and 0.94 for superpixel localization using an experimentally defined reduced CNN architecture based on the concept of InceptionV4. We notably achieve a lower false positive rate of 0.06 compared to prior work in the field presenting an efficient, robust and real-time solution for fire region detection.

Citation

Samarth, G., Bhowmik, N., & Breckon, T. (2019). Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. In M. . A. Wani, T. M. Khoshgoftaar, D. Wang, H. Wang, & N. (. Seliya (Eds.), Proceedings of the 18th IEEE International Conference on Machine Learning and Applications ICMLA 2019 (653-658). https://doi.org/10.1109/icmla.2019.00119

Conference Name 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019)
Conference Location Boca Raton, Florida, USA
Start Date Dec 16, 2019
End Date Dec 19, 2019
Acceptance Date Sep 21, 2019
Online Publication Date Feb 17, 2020
Publication Date Dec 16, 2019
Deposit Date Dec 20, 2019
Publicly Available Date Sep 4, 2020
Publisher Institute of Electrical and Electronics Engineers
Pages 653-658
Book Title Proceedings of the 18th IEEE International Conference on Machine Learning and Applications ICMLA 2019
DOI https://doi.org/10.1109/icmla.2019.00119
Public URL https://durham-repository.worktribe.com/output/1141363
Publisher URL https:/doi.org/10.1109/ICMLA.2019.00119

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Accepted Conference Proceeding (778 Kb)
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