English(EN) Obscured Wildfire Flame Detection by Spatio-temporal Analysis of Smoke Patterns Using Frame-wise Transformers

新型AI模型利用烟雾模式和Transformer检测野火

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一种新颖的深度学习方法用于野火检测，重点关注视频帧中烟雾模式的时空分析。该方法采用逐帧Transformer架构，在连续帧之间应用注意力机制，同时保留帧内的空间信息。该模型仅使用RGB视频进行训练，使其能够应用于低成本无人机和现有的仅RGB数据集，并使用红外辐射数据从FLAME2数据集中提取地面真实值。所提出的技术取得了高性能指标，包括92.61%的分割Dice分数和98.02%的剪辑级分类准确率。 AI

影响这项研究可能带来更有效、更具成本效益的野火检测系统，特别是对于配备标准摄像头的无人机。

排序理由该集群包含一篇详细介绍新AI模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Uma Meleti, Abolfazl Razi, Fatemeh Afghah · 2026-11-11 05:00

基于帧级Transformer的时空烟雾模式分析的遮挡野火火焰检测

arXiv:2611.14074v1 Announce Type: cross Abstract: In this paper, we propose a new paradigm in wildfire management for detecting both visible and obscured fire using Deep Learning (DL). The essence of our method is the joint spatial and temporal analysis of RGB videos, embracing t…

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基于帧级Transformer的时空烟雾模式分析的遮挡野火火焰检测

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