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Experimental exploration of compact convolutional neural network architectures for non-temporal real-time fire detection.

Samarth, G. and Bhowmik, N. and Breckon, T.P. (2019) 'Experimental exploration of compact convolutional neural network architectures for non-temporal real-time fire detection.', in Proceedings of the 18th IEEE International Conference on Machine Learning and Applications ICMLA 2019. Piscataway, NJ: IEEE, pp. 653-658.


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2019 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.
Date accepted:21 September 2019
Date deposited:29 December 2019
Date of first online publication:17 February 2020
Date first made open access:04 September 2020

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