Fire Superpixel Image Data Set for Samarth 2019 study - PNG still image set

Samarth, Ganesh (Institute of Technology Dharwad, India); Breckon, Toby (Durham University, UK); Bhowmik, Neelanjan (Durham University, UK)

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

Fire Superpixel Image Data Set for Samarth 2019 study - PNG still image set Open Access

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. | Cited in: Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (G. Samarth, N. Bhowmik, T.P. Breckon), In Proc. Int. Conf. on Machine Learning Applications, IEEE, 2019. | This file contains supporting materials in the form of the superpixel images used to train the neural network models.

Descriptions

Resource type: Dataset
Contributors: Creator: Samarth, Ganesh ¹
Contact person: Breckon, Toby ²
Editor: Bhowmik, Neelanjan ²

¹ Institute of Technology Dharwad, India
² Durham University, UK
Funder: Durham University
Research methods
Other description
Keyword: Convolutional Neural Network
fire detection
Subject: Computer Science
Engineering
Location: Durham, UK
Language: English
Cited in: Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (G. Samarth, N. Bhowmik, T.P. Breckon), In Proc. Int. Conf. on Machine Learning Applications, IEEE, 2019.
Identifier: ark:/32150/r10r967374q
doi:10.15128/r10r967374q
Rights: MIT Licence (MIT)

Creative Commons Attribution 4.0 International (CC BY)
Publisher: Durham University
Date Created: September 2019

File Details

Depositor: T. Breckon
Date Uploaded: 13 December 2019, 10:12:54
Date Modified: 24 January 2020, 10:01:53
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Characterization: File format: zip (ZIP Format)

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File size: 64579468

Last modified: 2019:12:13 10:59:08+00:00

Filename: fire-dataset-samarth.zip

Original checksum: 7dd2f5c92919e8d0d4dc75c4caa21f79

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