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Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

Thomson, W.; Bhowmik, N.; Breckon, T.P.

Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection Thumbnail


Authors

W. Thomson



Abstract

Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat). In this work, we investigate different Convolutional Neural Network (CNN) architectures and their variants for the non-temporal real-time bounds detection of fire pixel regions in video (or still) imagery. Two reduced complexity compact CNN architectures (NasNet-A-OnFire and ShuffleNetV2-OnFire) are proposed through experimental analysis to optimise the computational efficiency for this task. The results improve upon the current state-of-the-art solution for fire detection, achieving an accuracy of 95% for full-frame binary classification and 97% for superpixel localisation. We notably achieve a classification speed up by a factor of 2.3× for binary classification and 1.3× for superpixel localisation, with runtime of 40 fps and 18 fps respectively, outperforming prior work in the field presenting an efficient, robust and real-time solution for fire region detection. Subsequent implementation on low-powered devices (Nvidia Xavier-NX, achieving 49 fps for full-frame classification via ShuffleNetV2-OnFire) demonstrates our architectures are suitable for various real-world deployment applications.

Citation

Thomson, W., Bhowmik, N., & Breckon, T. (2021). Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. . https://doi.org/10.1109/icmla51294.2020.00030

Conference Name 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020)
Conference Location Miami, FL
Start Date Dec 14, 2020
End Date Dec 17, 2020
Acceptance Date Sep 16, 2020
Online Publication Date Feb 23, 2021
Publication Date 2021
Deposit Date Oct 26, 2020
Publicly Available Date Mar 29, 2024
Publisher Institute of Electrical and Electronics Engineers
DOI https://doi.org/10.1109/icmla51294.2020.00030

Files

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