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A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing
Accurate fire identification can help to control fires. Traditional fire detection methods are mainly based on temperature or smoke detectors. These detectors are susceptible to damage or interference from the outside environment. Meanwhile, most of the current deep learning methods are less discrim...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025736/ https://www.ncbi.nlm.nih.gov/pubmed/35458913 http://dx.doi.org/10.3390/s22082929 |
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author | An, Qing Chen, Xijiang Zhang, Junqian Shi, Ruizhe Yang, Yuanjun Huang, Wei |
author_facet | An, Qing Chen, Xijiang Zhang, Junqian Shi, Ruizhe Yang, Yuanjun Huang, Wei |
author_sort | An, Qing |
collection | PubMed |
description | Accurate fire identification can help to control fires. Traditional fire detection methods are mainly based on temperature or smoke detectors. These detectors are susceptible to damage or interference from the outside environment. Meanwhile, most of the current deep learning methods are less discriminative with respect to dynamic fire and have lower detection precision when a fire changes. Therefore, we propose a dynamic convolution YOLOv5 fire detection method using a video sequence. Our method first uses the K-mean++ algorithm to optimize anchor box clustering; this significantly reduces the rate of classification error. Then, the dynamic convolution is introduced into the convolution layer of YOLOv5. Finally, pruning of the network heads of YOLOv5’s neck and head is carried out to improve the detection speed. Experimental results verify that the proposed dynamic convolution YOLOv5 fire detection method demonstrates better performance than the YOLOv5 method in recall, precision and F1-score. In particular, compared with three other deep learning methods, the precision of the proposed algorithm is improved by 13.7%, 10.8% and 6.1%, respectively, while the F1-score is improved by 15.8%, 12% and 3.8%, respectively. The method described in this paper is applicable not only to short-range indoor fire identification but also to long-range outdoor fire detection. |
format | Online Article Text |
id | pubmed-9025736 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90257362022-04-23 A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing An, Qing Chen, Xijiang Zhang, Junqian Shi, Ruizhe Yang, Yuanjun Huang, Wei Sensors (Basel) Article Accurate fire identification can help to control fires. Traditional fire detection methods are mainly based on temperature or smoke detectors. These detectors are susceptible to damage or interference from the outside environment. Meanwhile, most of the current deep learning methods are less discriminative with respect to dynamic fire and have lower detection precision when a fire changes. Therefore, we propose a dynamic convolution YOLOv5 fire detection method using a video sequence. Our method first uses the K-mean++ algorithm to optimize anchor box clustering; this significantly reduces the rate of classification error. Then, the dynamic convolution is introduced into the convolution layer of YOLOv5. Finally, pruning of the network heads of YOLOv5’s neck and head is carried out to improve the detection speed. Experimental results verify that the proposed dynamic convolution YOLOv5 fire detection method demonstrates better performance than the YOLOv5 method in recall, precision and F1-score. In particular, compared with three other deep learning methods, the precision of the proposed algorithm is improved by 13.7%, 10.8% and 6.1%, respectively, while the F1-score is improved by 15.8%, 12% and 3.8%, respectively. The method described in this paper is applicable not only to short-range indoor fire identification but also to long-range outdoor fire detection. MDPI 2022-04-11 /pmc/articles/PMC9025736/ /pubmed/35458913 http://dx.doi.org/10.3390/s22082929 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article An, Qing Chen, Xijiang Zhang, Junqian Shi, Ruizhe Yang, Yuanjun Huang, Wei A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing |
title | A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing |
title_full | A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing |
title_fullStr | A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing |
title_full_unstemmed | A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing |
title_short | A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing |
title_sort | robust fire detection model via convolution neural networks for intelligent robot vision sensing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025736/ https://www.ncbi.nlm.nih.gov/pubmed/35458913 http://dx.doi.org/10.3390/s22082929 |
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