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KutralNet: A Portable Deep Learning Model for Fire Recognition
conference contribution
posted on 2020-01-01, 00:00 authored by A Ayala, B Fernandes, F Cruz Naranjo, D MacEdo, A L I Oliveira, C ZanchettinMost of the automatic fire alarm systems detect the fire presence through sensors like thermal, smoke, or flame. One of the new approaches to the problem is the use of images to perform the detection. The image approach is promising since it does not need specific sensors and can be easily embedded in different devices. However, besides the high performance, the computational cost of the used deep learning methods is a challenge to their deployment in portable devices. In this work, we propose a new deep learning architecture that requires fewer floating-point operations (flops) for fire recognition. Additionally, we propose a portable approach for fire recognition and the use of modern techniques such as inverted residual block, convolutions like depth-wise, and octave, to reduce the model's computational cost. The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops. One of our models presents 71% fewer parameters than FireNet, while still presenting competitive accuracy and AUROC performance. The proposed methods are evaluated on FireNet and FiSmo datasets. The obtained results are promising for the implementation of the model in a mobile device, considering the reduced number of flops and parameters acquired.
History
Event
Neural Networks. International Joint Conference (2020 : Online from Glasgow, Scot.)Pagination
1 - 8Publisher
IEEELocation
Online from Glasgow, ScotlandPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2020-07-19End date
2020-07-24ISSN
2161-4393eISSN
2161-4407ISBN-13
9781728169262Language
engNotes
The 2020 International Joint Conference on Neural Networks (IJCNN) was held virtually, as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020Publication classification
E1 Full written paper - refereedEditor/Contributor(s)
[Unknown]Title of proceedings
IJCNN 2020 : Proceedings of the 2020 International Joint conference on Neural NetworksUsage metrics
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