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Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning
To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained iden...
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/PMC9658153/ https://www.ncbi.nlm.nih.gov/pubmed/36366081 http://dx.doi.org/10.3390/s22218383 |
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author | Sun, Bingjian Cheng, Pengle Huang, Ying |
author_facet | Sun, Bingjian Cheng, Pengle Huang, Ying |
author_sort | Sun, Bingjian |
collection | PubMed |
description | To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database. In this paper, for the first time, we combine fine-grained smoke recognition with the few-shot technique using metric learning to identify fire smoke with the limited available database. The experimental comparison and analysis show that the new method developed has good performance in the structure of the feature extraction network and the training method, with an accuracy of 93.75% for fire smoke identification. |
format | Online Article Text |
id | pubmed-9658153 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96581532022-11-15 Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning Sun, Bingjian Cheng, Pengle Huang, Ying Sensors (Basel) Article To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database. In this paper, for the first time, we combine fine-grained smoke recognition with the few-shot technique using metric learning to identify fire smoke with the limited available database. The experimental comparison and analysis show that the new method developed has good performance in the structure of the feature extraction network and the training method, with an accuracy of 93.75% for fire smoke identification. MDPI 2022-11-01 /pmc/articles/PMC9658153/ /pubmed/36366081 http://dx.doi.org/10.3390/s22218383 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 Sun, Bingjian Cheng, Pengle Huang, Ying Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning |
title | Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning |
title_full | Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning |
title_fullStr | Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning |
title_full_unstemmed | Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning |
title_short | Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning |
title_sort | few-shot fine-grained forest fire smoke recognition based on metric learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9658153/ https://www.ncbi.nlm.nih.gov/pubmed/36366081 http://dx.doi.org/10.3390/s22218383 |
work_keys_str_mv | AT sunbingjian fewshotfinegrainedforestfiresmokerecognitionbasedonmetriclearning AT chengpengle fewshotfinegrainedforestfiresmokerecognitionbasedonmetriclearning AT huangying fewshotfinegrainedforestfiresmokerecognitionbasedonmetriclearning |