A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles wi...

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Autores principales:	Zewei Wang, Change Zheng, Jiyan Yin, Ye Tian, Wenbin Cui
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	forest fire smoke semantic segmentation the weighted method labeling ambiguity Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/9fd81efbf5e346bba6d1a0b03d23ede1
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id	oai:doaj.org-article:9fd81efbf5e346bba6d1a0b03d23ede1
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spelling	oai:doaj.org-article:9fd81efbf5e346bba6d1a0b03d23ede12021-11-11T15:40:00ZA Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting10.3390/electronics102126752079-9292https://doaj.org/article/9fd81efbf5e346bba6d1a0b03d23ede12021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2675https://doaj.org/toc/2079-9292Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.Zewei WangChange ZhengJiyan YinYe TianWenbin CuiMDPI AGarticleforest fire smokesemantic segmentationthe weighted methodlabeling ambiguityElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2675, p 2675 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	forest fire smoke semantic segmentation the weighted method labeling ambiguity Electronics TK7800-8360
spellingShingle	forest fire smoke semantic segmentation the weighted method labeling ambiguity Electronics TK7800-8360 Zewei Wang Change Zheng Jiyan Yin Ye Tian Wenbin Cui A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting
description	Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.
format	article
author	Zewei Wang Change Zheng Jiyan Yin Ye Tian Wenbin Cui
author_facet	Zewei Wang Change Zheng Jiyan Yin Ye Tian Wenbin Cui
author_sort	Zewei Wang
title	A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting
title_short	A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting
title_full	A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting
title_fullStr	A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting
title_full_unstemmed	A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting
title_sort	semantic segmentation method for early forest fire smoke based on concentration weighting
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9fd81efbf5e346bba6d1a0b03d23ede1
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A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

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