SMWE-GFPNNet: A high-precision and robust method for forest fire smoke detection

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Li, Rui;Hu, Yaowen;Li, Lin;Guan, Renxiang;Yang, Ruoli;...

通讯作者：

Li, L

作者机构：

[Li, Lin; Zhan, Jialei; Li, Rui; Li, L; Yang, Ruoli; Hu, Yaowen; Cai, Weiwei] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.

[Guan, Renxiang; Hu, Yaowen] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China.

[Zhan, Jialei; Wang, Yanfeng; Yang, Ruoli] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China.

[Xu, Haiwen] Hunan Forest Grassland Fire Prevent Monitoring Dis, Changsha, Hunan, Peoples R China.

[Li, Liujun] Univ Idaho, Dept Soil & Water Syst, Moscow, ID 83844 USA.

通讯机构：

[Li, L ] C

Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.

语种：

英文

关键词：

Forest fire smoke detection;Feature pyramid network;Loss function;Feature extraction;Deep learning

期刊：

Knowledge-Based Systems

ISSN：

0950-7051

年：

2024

卷：

289

页码：

111528

DOI：

10.1016/j.knosys.2024.111528

基金类别：

CRediT authorship contribution statement Rui Li: Software, Writing – original draft, Writing – review & editing. Yaowen Hu: Methodology, Writing – original draft. Lin Li: Supervision, acquisition. Renxiang Guan: Data curation, Formal analysis. Ruoli Yang: Validation. Jialei Zhan: Data curation. Weiwei Cai: Investigation. Yanfeng Wang: Formal analysis, Resources. Haiwen Xu: Supervision. Liujun Li: Visualization. The main funding for this study comes from the National Natural Science Foundation in China (Grant No. 61902436), the Natural Science Foundation of Changsha (Grant No. KQ2014160), the Natural Science Foundation of Hunan Province (Grant No. 2021JJ41077) and the Hunan Provincial Department of Education Research Project (Grant No. 21A0179).

机构署名：

本校为第一且通讯机构

院系归属：

计算机与信息工程学院

摘要：

Smoke is an early manifestation of forest fire. Accurate identification of smoke from forest fires is crucial for the prevention and control of forest fires, which helps protect the ecological environment and the safety of people. The texture features of smoke are complex and prone to detection omissions. The forest environment is complex, and smoke-like objects in the forest often interfere with smoke recognition. The concentration of smoke at the edge is thin, which easily leads to edge omission. In response to these problems, we propose a high-precision edge focused forest fire smoke detect...

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SMWE-GFPNNet: A high-precision and robust method for forest fire smoke detection

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