Pretrained Neural Network Models for Thomson 2020 study - PyTorch format

Bhowmik, Neelanjan (Durham University, UK); Breckon, Toby P. (Durham University, UK); Thomson, William (Durham University, UK)

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

Pretrained Neural Network Models for Thomson 2020 study - PyTorch format Open Access

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 nontemporal 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. | Cited in: Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (W. Thomson, N. Bhowmik, T.P. Breckon), In Proc. Int. Conf. on Machine Learning Applications, IEEE, 2020. | This file contains supporting materials in the form of the pre-trained network models used in the study.

Descriptions

Resource type: Other
Contributors: Creator: Thomson, William ¹
Contact person: Bhowmik, Neelanjan ¹
Editor: Bhowmik, Neelanjan ¹
Editor: Breckon, Toby P. ¹

¹ Durham University, UK
Funder: Durham University
Research methods
Other description
Keyword: Convolutional Neural Network
fire detection
classification
Subject: Engineering
Computer science
Location: Durham, England, United Kingdom
Language: English
Cited in: Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (W. Thomson, N. Bhowmik, T.P. Breckon), In Proc. Int. Conf. on Machine Learning Applications, IEEE, 2020
Identifier: ark:/32150/r1tb09j570z
doi:10.15128/r1tb09j570z
Rights: MIT Licence (MIT)

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

File Details

Depositor: N. Bhowmik
Date Uploaded: 18 January 2021, 19:01:56
Date Modified: 7 March 2021, 19:03:43
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Last modified: 2021:03:07 19:17:00+00:00

Filename: thomson-2020-fire-detection-pretrained-models-pytorch--version2.zip

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