Skip to Content

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
Date Modified
24 January 2020, 10:01:53
Audit Status
Audits have not yet been run on this file.
Characterization
File format: zip (ZIP Format)
Mime type: application/zip
File size: 64579468
Last modified: 2019:12:13 10:59:08+00:00
Filename: fire-dataset-samarth.zip
Original checksum: 7dd2f5c92919e8d0d4dc75c4caa21f79
Activity of users you follow
User Activity Date
User N. Syrotiuk has updated Fire Superpixel Image Data Set for Samarth 2019 study - PNG still image set 6 months ago
User T. Breckon has updated Fire Superpixel Image Data Set for Samarth 2019 study - PNG still image set 6 months ago
User T. Breckon has deposited fire-dataset-samarth.zip 6 months ago