森林火灾检测算法研究综述

A Review of Forest Fire Detection Algorithms

摘要: 随着人工智能技术领域的快速发展，面对森林火灾是造成全球森林损失和人员伤害并影响森林生态系统安全的重大森林灾害。所以，有必要对森林火灾进行识别研究，为后续救援提供条件。深度学习提供了一种有效、精准、智能化的检测算法，通过实时监测与快速响应，有效防控森林火情，筑牢生态安全屏障，守护绿色家园。本文将介绍森林火灾检测领域常用数据集以及详细阐述SSD算法、YOLO算法、Faster R-CNN算法三种算法的基本原理，以及简要阐述目前基于无人机图像检测结合各种算法的应用场景及其研究现状及其存在的不足，并对进一步研究进行了展望。

Abstract: With the rapid development of artificial intelligence technology, forest fires have become a major forest disaster that causes global forest losses and human injuries and affects the security of forest ecosystems. Therefore, it is necessary to conduct identification research on forest fires to provide conditions for subsequent rescue operations. Deep learning offers an effective, precise and intelligent detection algorithm. Through real-time monitoring and rapid response, it can effectively prevent and control forest fires, build a solid ecological security barrier and safeguard the green homeland. This paper will introduce the commonly used datasets in the field of forest fire detection and elaborate in detail the basic principles of three algorithms: SSD algorithm, YOLO algorithm, and Faster R-CNN algorithm. It will also briefly describe the current application scenarios based on unmanned aerial vehicle image detection combined with various algorithms, as well as their research status and existing deficiencies, and make prospects for further research.