A. Dunnings
Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection
Dunnings, A.; Breckon, T.P.
Abstract
In this work we investigate the automatic detection of fire pixel regions in video (or still) imagery within real-time bounds without reliance on temporal scene information. As an extension to prior work in the field, we consider the performance of experimentally defined, reduced complexity deep convolutional neural network architectures for this task. Contrary to contemporary trends in the field, our work illustrates maximal accuracy of 0.93 for whole image binary fire detection, with 0.89 accuracy within our superpixel localization framework can be achieved, via a network architecture of signficantly reduced complexity. These reduced architectures additionally offer a 3–4 fold increase in computational performance offering up to 17 fps processing on contemporary hardware independent of temporal information. We show the relative performance achieved against prior work using benchmark datasets to illustrate maximally robust real-time fire region detection.
Citation
Dunnings, A., & Breckon, T. (2018, October). Experimentally Defined Convolutional Neural Network Architecture Variants for Non-temporal Real-time Fire Detection. Presented at 25th IEEE International Conference on Image Processing (ICIP)., Athens, Greece
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 25th IEEE International Conference on Image Processing (ICIP). |
Start Date | Oct 7, 2018 |
End Date | Oct 10, 2018 |
Acceptance Date | May 30, 2018 |
Online Publication Date | Sep 6, 2018 |
Publication Date | 2018 |
Deposit Date | May 30, 2018 |
Publicly Available Date | Jul 27, 2018 |
Pages | 1558-1562 |
Series ISSN | 2381-8549 |
Book Title | Proc. Int. Conf. on Image Processing |
ISBN | 9781479970629 |
DOI | https://doi.org/10.1109/ICIP.2018.8451657 |
Keywords | fire detection, CNN, deep-learning real-time, non-temporal |
Public URL | https://durham-repository.worktribe.com/output/1146797 |
Publisher URL | https://breckon.org/toby/publications/papers/dunnings18fire.pdf |
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Revised version © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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