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ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution

In this paper, we propose an anchor-free smoke and fire detection network, ADFireNet, based on deformable convolution. The proposed ADFireNet network is composed of three parts: The backbone network is responsible for feature extraction of input images, which is composed of ResNet added to deformabl...

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Detalles Bibliográficos
Autores principales: Li, Bin, Liu, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458128/
https://www.ncbi.nlm.nih.gov/pubmed/37631624
http://dx.doi.org/10.3390/s23167086
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author Li, Bin
Liu, Peng
author_facet Li, Bin
Liu, Peng
author_sort Li, Bin
collection PubMed
description In this paper, we propose an anchor-free smoke and fire detection network, ADFireNet, based on deformable convolution. The proposed ADFireNet network is composed of three parts: The backbone network is responsible for feature extraction of input images, which is composed of ResNet added to deformable convolution. The neck network, which is responsible for multi-scale detection, is composed of the feature pyramid network. The head network outputs results and adopts pseudo intersection over union combined with anchor-free network structure. The head network consists of two full convolutional subnetworks: the first is the classification sub-network, which outputs a classification confidence score, and the second is the regression sub-network, which predicts the parameters of bounding boxes. The deformable convolution (DCN) added to the backbone network enhances the shape feature extraction capability for fire and smoke, and the pseudo intersection over union (pseudo-IoU) added to the head network solves the label assignment problem that exists in anchor-free object detection networks. The proposed ADFireNet is evaluated using the fire smoke dataset. The experimental results show that ADFireNet has higher accuracy and faster detection speeds compared with other methods. Ablation studies have demonstrated the effectiveness of DCN and pseudo IoU.
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spelling pubmed-104581282023-08-27 ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution Li, Bin Liu, Peng Sensors (Basel) Article In this paper, we propose an anchor-free smoke and fire detection network, ADFireNet, based on deformable convolution. The proposed ADFireNet network is composed of three parts: The backbone network is responsible for feature extraction of input images, which is composed of ResNet added to deformable convolution. The neck network, which is responsible for multi-scale detection, is composed of the feature pyramid network. The head network outputs results and adopts pseudo intersection over union combined with anchor-free network structure. The head network consists of two full convolutional subnetworks: the first is the classification sub-network, which outputs a classification confidence score, and the second is the regression sub-network, which predicts the parameters of bounding boxes. The deformable convolution (DCN) added to the backbone network enhances the shape feature extraction capability for fire and smoke, and the pseudo intersection over union (pseudo-IoU) added to the head network solves the label assignment problem that exists in anchor-free object detection networks. The proposed ADFireNet is evaluated using the fire smoke dataset. The experimental results show that ADFireNet has higher accuracy and faster detection speeds compared with other methods. Ablation studies have demonstrated the effectiveness of DCN and pseudo IoU. MDPI 2023-08-10 /pmc/articles/PMC10458128/ /pubmed/37631624 http://dx.doi.org/10.3390/s23167086 Text en © 2023 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
Li, Bin
Liu, Peng
ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution
title ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution
title_full ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution
title_fullStr ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution
title_full_unstemmed ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution
title_short ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution
title_sort adfirenet: an anchor-free smoke and fire detection network based on deformable convolution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458128/
https://www.ncbi.nlm.nih.gov/pubmed/37631624
http://dx.doi.org/10.3390/s23167086
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