Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges

With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and bl...
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Autor*in:	Guodong Wang [verfasserIn] Fang Wang [verfasserIn] Hongping Zhou [verfasserIn] Haifeng Lin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	forest fire Swin Transformer adaptive multi-scale attention mechanism (ASA) semantic segmentation wildfire monitoring

Übergeordnetes Werk:	In: Forests - MDPI AG, 2010, 15(2024), 1, p 217
Übergeordnetes Werk:	volume:15 ; year:2024 ; number:1, p 217

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/f15010217

Katalog-ID:	DOAJ096361646

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language	English
source	In Forests 15(2024), 1, p 217 volume:15 year:2024 number:1, p 217
sourceStr	In Forests 15(2024), 1, p 217 volume:15 year:2024 number:1, p 217
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	forest fire Swin Transformer adaptive multi-scale attention mechanism (ASA) semantic segmentation wildfire monitoring Plant ecology
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container_title	Forests
authorswithroles_txt_mv	Guodong Wang @@aut@@ Fang Wang @@aut@@ Hongping Zhou @@aut@@ Haifeng Lin @@aut@@
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callnumber-first	Q - Science
author	Guodong Wang
spellingShingle	Guodong Wang misc QK900-989 misc forest fire misc Swin Transformer misc adaptive multi-scale attention mechanism (ASA) misc semantic segmentation misc wildfire monitoring misc Plant ecology Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges
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topic_title	QK900-989 Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges forest fire Swin Transformer adaptive multi-scale attention mechanism (ASA) semantic segmentation wildfire monitoring
topic	misc QK900-989 misc forest fire misc Swin Transformer misc adaptive multi-scale attention mechanism (ASA) misc semantic segmentation misc wildfire monitoring misc Plant ecology
topic_unstemmed	misc QK900-989 misc forest fire misc Swin Transformer misc adaptive multi-scale attention mechanism (ASA) misc semantic segmentation misc wildfire monitoring misc Plant ecology
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title	Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges
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title_sort	fire in focus: advancing wildfire image segmentation by focusing on fire edges
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title_auth	Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges
abstract	With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred boundaries of flames and smoke, traditional convolutional neural networks (CNNs) face limitations in forest fire image segmentation, including flame edge recognition, class imbalance issues, and adapting to complex scenarios. This study aims to enhance the accuracy and efficiency of flame recognition in forest fire images by introducing a backbone network based on the Swin Transformer and combined with an adaptive multi-scale attention mechanism and focal loss function. By utilizing a rich and diverse pre-training dataset, our model can more effectively capture and understand key features of forest fire images. Through experimentation, our model achieved an intersection over union (IoU) of 86.73% and a precision of 91.23%. This indicates that the performance of our proposed wildfire segmentation model has been effectively enhanced. A series of ablation experiments validate the importance of these technological improvements in enhancing model performance. The results show that our approach achieves significant performance improvements in forest fire image segmentation tasks compared to traditional models. The Swin Transformer provides more refined feature extraction capabilities, the adaptive multi-scale attention mechanism helps the model focus better on key areas, and the focal loss function effectively addresses the issue of class imbalance. These innovations make the model more precise and robust in handling forest fire image segmentation tasks, providing strong technical support for future forest fire monitoring and prevention.
abstractGer	With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred boundaries of flames and smoke, traditional convolutional neural networks (CNNs) face limitations in forest fire image segmentation, including flame edge recognition, class imbalance issues, and adapting to complex scenarios. This study aims to enhance the accuracy and efficiency of flame recognition in forest fire images by introducing a backbone network based on the Swin Transformer and combined with an adaptive multi-scale attention mechanism and focal loss function. By utilizing a rich and diverse pre-training dataset, our model can more effectively capture and understand key features of forest fire images. Through experimentation, our model achieved an intersection over union (IoU) of 86.73% and a precision of 91.23%. This indicates that the performance of our proposed wildfire segmentation model has been effectively enhanced. A series of ablation experiments validate the importance of these technological improvements in enhancing model performance. The results show that our approach achieves significant performance improvements in forest fire image segmentation tasks compared to traditional models. The Swin Transformer provides more refined feature extraction capabilities, the adaptive multi-scale attention mechanism helps the model focus better on key areas, and the focal loss function effectively addresses the issue of class imbalance. These innovations make the model more precise and robust in handling forest fire image segmentation tasks, providing strong technical support for future forest fire monitoring and prevention.
abstract_unstemmed	With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred boundaries of flames and smoke, traditional convolutional neural networks (CNNs) face limitations in forest fire image segmentation, including flame edge recognition, class imbalance issues, and adapting to complex scenarios. This study aims to enhance the accuracy and efficiency of flame recognition in forest fire images by introducing a backbone network based on the Swin Transformer and combined with an adaptive multi-scale attention mechanism and focal loss function. By utilizing a rich and diverse pre-training dataset, our model can more effectively capture and understand key features of forest fire images. Through experimentation, our model achieved an intersection over union (IoU) of 86.73% and a precision of 91.23%. This indicates that the performance of our proposed wildfire segmentation model has been effectively enhanced. A series of ablation experiments validate the importance of these technological improvements in enhancing model performance. The results show that our approach achieves significant performance improvements in forest fire image segmentation tasks compared to traditional models. The Swin Transformer provides more refined feature extraction capabilities, the adaptive multi-scale attention mechanism helps the model focus better on key areas, and the focal loss function effectively addresses the issue of class imbalance. These innovations make the model more precise and robust in handling forest fire image segmentation tasks, providing strong technical support for future forest fire monitoring and prevention.
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title_short	Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges
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