利用WRF数值预报产品进行野火摄影遥测的综合方法

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL Photogrammetric Engineering and Remote Sensing Pub Date : 2023-11-01 DOI:10.14358/pers.23-00047r2
Ling Tan, Xuelan Ma
{"title":"利用WRF数值预报产品进行野火摄影遥测的综合方法","authors":"Ling Tan, Xuelan Ma","doi":"10.14358/pers.23-00047r2","DOIUrl":null,"url":null,"abstract":"Forest fire detection using machine vision has recently emerged as a hot research topic. However, the complexity of background information in smoke images often results in deep learning models losing crucial details while capturing smoke image features. To address this, we present a detection algorithm called Multichannel Smoke YOLOv5s (MCSYOLOv5s). This algorithm comprises a smoke flame detection module, multichannel YOLOv5s (MC‐YOLOv5s), and a smoke cloud classification module, Smoke Classification Network (SCN). MC‐YOLOv5s uses a generative confrontation structure to design a dual‐channel feature extraction network and adopts a new feature cross-fusion mechanism to enhance the smoke feature extraction ability of classic YOLOv5s. The SCN module combines Weather Research and Forecasting numerical forecast results to classify smoke and clouds to reduce false positives caused by clouds. Experimental results demonstrate that our proposed forest fire monitoring method, MCS‐YOLOv5s, achieves higher detection accuracy of 95.17%, surpassing all comparative algorithms. Moreover, it effectively reduces false alarms caused by clouds.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Integrated Approach for Wildfire Photography Telemetry using WRF Numerical Forecast Products\",\"authors\":\"Ling Tan, Xuelan Ma\",\"doi\":\"10.14358/pers.23-00047r2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Forest fire detection using machine vision has recently emerged as a hot research topic. However, the complexity of background information in smoke images often results in deep learning models losing crucial details while capturing smoke image features. To address this, we present a detection algorithm called Multichannel Smoke YOLOv5s (MCSYOLOv5s). This algorithm comprises a smoke flame detection module, multichannel YOLOv5s (MC‐YOLOv5s), and a smoke cloud classification module, Smoke Classification Network (SCN). MC‐YOLOv5s uses a generative confrontation structure to design a dual‐channel feature extraction network and adopts a new feature cross-fusion mechanism to enhance the smoke feature extraction ability of classic YOLOv5s. The SCN module combines Weather Research and Forecasting numerical forecast results to classify smoke and clouds to reduce false positives caused by clouds. Experimental results demonstrate that our proposed forest fire monitoring method, MCS‐YOLOv5s, achieves higher detection accuracy of 95.17%, surpassing all comparative algorithms. Moreover, it effectively reduces false alarms caused by clouds.\",\"PeriodicalId\":49702,\"journal\":{\"name\":\"Photogrammetric Engineering and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photogrammetric Engineering and Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14358/pers.23-00047r2\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photogrammetric Engineering and Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14358/pers.23-00047r2","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

摘要

利用机器视觉进行森林火灾探测是近年来的一个研究热点。然而,烟雾图像背景信息的复杂性往往导致深度学习模型在捕捉烟雾图像特征时失去关键细节。为了解决这个问题,我们提出了一种称为多通道烟雾YOLOv5s (MCSYOLOv5s)的检测算法。该算法包括烟雾火焰检测模块,多通道YOLOv5s (MC‐YOLOv5s)和烟雾云分类模块,烟雾分类网络(SCN)。MC‐YOLOv5s采用生成对抗结构设计了双通道特征提取网络,并采用了新的特征交叉融合机制,增强了经典YOLOv5s的烟雾特征提取能力。SCN模块结合天气研究和预报的数值预报结果,对烟雾和云进行分类,以减少云造成的误报。实验结果表明,我们提出的森林火灾监测方法MCS‐YOLOv5s的检测准确率达到95.17%,超过了所有的比较算法。此外,它有效地减少了由云引起的误报警。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An Integrated Approach for Wildfire Photography Telemetry using WRF Numerical Forecast Products
Forest fire detection using machine vision has recently emerged as a hot research topic. However, the complexity of background information in smoke images often results in deep learning models losing crucial details while capturing smoke image features. To address this, we present a detection algorithm called Multichannel Smoke YOLOv5s (MCSYOLOv5s). This algorithm comprises a smoke flame detection module, multichannel YOLOv5s (MC‐YOLOv5s), and a smoke cloud classification module, Smoke Classification Network (SCN). MC‐YOLOv5s uses a generative confrontation structure to design a dual‐channel feature extraction network and adopts a new feature cross-fusion mechanism to enhance the smoke feature extraction ability of classic YOLOv5s. The SCN module combines Weather Research and Forecasting numerical forecast results to classify smoke and clouds to reduce false positives caused by clouds. Experimental results demonstrate that our proposed forest fire monitoring method, MCS‐YOLOv5s, achieves higher detection accuracy of 95.17%, surpassing all comparative algorithms. Moreover, it effectively reduces false alarms caused by clouds.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Photogrammetric Engineering and Remote Sensing
Photogrammetric Engineering and Remote Sensing 地学-成像科学与照相技术
CiteScore
1.70
自引率
15.40%
发文量
89
审稿时长
9 months
期刊介绍: Photogrammetric Engineering & Remote Sensing commonly referred to as PE&RS, is the official journal of imaging and geospatial information science and technology. Included in the journal on a regular basis are highlight articles such as the popular columns “Grids & Datums” and “Mapping Matters” and peer reviewed technical papers. We publish thousands of documents, reports, codes, and informational articles in and about the industries relating to Geospatial Sciences, Remote Sensing, Photogrammetry and other imaging sciences.
期刊最新文献
A Powerful Correspondence Selection Method for Point Cloud Registration Based on Machine Learning Identification of Critical Urban Clusters for Placating Urban Heat Island Effects over Fast-Growing Tropical City Regions: Estimating the Contribution of Different City Sizes in Escalating UHI Intensity A Novel Object Detection Method for Solid Waste Incorporating a Weighted Deformable Convolution GIS Tips & Tricks ‐ Relationships Count when Mapping? An Integrated Approach for Wildfire Photography Telemetry using WRF Numerical Forecast Products
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1