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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10458128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT libin adfirenetananchorfreesmokeandfiredetectionnetworkbasedondeformableconvolution AT liupeng adfirenetananchorfreesmokeandfiredetectionnetworkbasedondeformableconvolution |