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An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images
Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, incl...
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/PMC10610991/ https://www.ncbi.nlm.nih.gov/pubmed/37896467 http://dx.doi.org/10.3390/s23208374 |
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author | Saydirasulovich, Saydirasulov Norkobil Mukhiddinov, Mukhriddin Djuraev, Oybek Abdusalomov, Akmalbek Cho, Young-Im |
author_facet | Saydirasulovich, Saydirasulov Norkobil Mukhiddinov, Mukhriddin Djuraev, Oybek Abdusalomov, Akmalbek Cho, Young-Im |
author_sort | Saydirasulovich, Saydirasulov Norkobil |
collection | PubMed |
description | Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model’s capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model’s attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model’s effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model’s performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively. |
format | Online Article Text |
id | pubmed-10610991 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106109912023-10-28 An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images Saydirasulovich, Saydirasulov Norkobil Mukhiddinov, Mukhriddin Djuraev, Oybek Abdusalomov, Akmalbek Cho, Young-Im Sensors (Basel) Article Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model’s capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model’s attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model’s effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model’s performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively. MDPI 2023-10-10 /pmc/articles/PMC10610991/ /pubmed/37896467 http://dx.doi.org/10.3390/s23208374 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 Saydirasulovich, Saydirasulov Norkobil Mukhiddinov, Mukhriddin Djuraev, Oybek Abdusalomov, Akmalbek Cho, Young-Im An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images |
title | An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images |
title_full | An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images |
title_fullStr | An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images |
title_full_unstemmed | An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images |
title_short | An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images |
title_sort | improved wildfire smoke detection based on yolov8 and uav images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610991/ https://www.ncbi.nlm.nih.gov/pubmed/37896467 http://dx.doi.org/10.3390/s23208374 |
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