An Integrated Approach for Wildfire Photography Telemetry using WRF Numerical Forecast Products

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL
Ling Tan, Xuelan Ma
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引用次数: 0

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.
利用WRF数值预报产品进行野火摄影遥测的综合方法
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。
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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.
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