Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model

doi:10.3390/app14062413

IR > 理学院

	Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
	Yang, Zhaoxu 1; Shao, Yifan 1; Wei, Ye 1; Li, Jun2
	2024-03
发表期刊	APPLIED SCIENCES-BASEL
卷号	14 期号:6
摘要	Forest fires present a significant challenge to ecosystems, particularly due to factors like tree cover that complicate fire detection tasks. While fire detection technologies, like YOLO, are widely used in forest protection, capturing diverse and complex flame features remains challenging. Therefore, we propose an enhanced YOLOv8 multiscale forest fire detection method. This involves adjusting the network structure and integrating Deformable Convolution and SCConv modules to better adapt to forest fire complexities. Additionally, we introduce the Coordinate Attention mechanism in the Detection module to more effectively capture feature information and enhance model accuracy. We adopt the WIoU v3 loss function and implement a dynamically non-monotonic mechanism to optimize gradient allocation strategies. Our experimental results demonstrate that our model achieves a mAP of 90.02%, approximately 5.9% higher than the baseline YOLOv8 network. This method significantly improves forest fire detection accuracy, reduces False Positive rates, and demonstrates excellent applicability in real forest fire scenarios.
关键词	forest fire detection YOLOv8 SCConv deformable convolution CoordAtt mechanism WIoU v3
DOI	10.3390/app14062413
收录类别	SCIE
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:001191568600001
出版者	MDPI
原始文献类型	Article
EISSN	2076-3417
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/170187
专题	理学院
通讯作者	Li, Jun
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun Technol, Lanzhou 730050, Peoples R China; 2.Lanzhou Univ Technol, Dept Appl Math, Lanzhou 730050, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	材料科学与工程学院
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Yang, Zhaoxu,Shao, Yifan,Wei, Ye,et al. Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model[J]. APPLIED SCIENCES-BASEL,2024,14(6).
APA	Yang, Zhaoxu,Shao, Yifan,Wei, Ye,&Li, Jun.(2024).Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model.APPLIED SCIENCES-BASEL,14(6).
MLA	Yang, Zhaoxu,et al."Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model".APPLIED SCIENCES-BASEL 14.6(2024).