摘要
基于深度学习技术的火灾检测方法迅速发展,为森林火灾检测提供了新的发展方向。介绍了深度学习的概念、主要模型及在森林火灾检测中的优势,详细阐述了不同深度学习模型在森林火灾烟雾与火焰特征的提取、火灾与非火灾图像的分类、火灾的预判跟踪中的应用,并对当前方法进行了总结分析。最后,提出了目前存在的问题以及未来的研究方向,以期为森林火灾检测提供新思路。
The rapid development of fire detection methods based on deep learning technology provides a new development direction for forest fire detection.In this paper,the concept,main models and advantages of deep learning in forest fire detection were introduced,the application of different deep learning models in forest fire smoke and flame feature extraction,fire and non-fire image classification,fire prediction and tracking was elaborated,and the current methods were summarized and analyzed.Finally,the current problems and future research directions were discussed in order to provide new ideas for forest fire detection.
作者
王丽霞
夏雪
高凡
刘强
董霙达
郜晓晶
WANG Li-xia;XIA Xue;GAO Fan;LIU Qiang;DONG Ying-da;GAO Xiao-jing(School of Computer and Information Engineering,Inner Mongolia Agricultural University,Hohhot 010018,Inner Mongolia,P.R.China)
出处
《林产工业》
北大核心
2023年第11期88-92,共5页
China Forest Products Industry
基金
内蒙古自治区自然科学基金项目(2021MS03087)
内蒙高等学校科学研究项目“基于特征融合的多形态可见光牧草图像识别技术的研究”(NJZY21492)。
关键词
森林火灾检测
深度学习
特征提取
图像分类
预判跟踪
Forest fire detection
Deep learning
Feature extraction
Image classification
Predictive tracking
