用于早期火灾烟雾探测的超轻量级卷积变换器网络

IF 3.6 3区 环境科学与生态学 Q1 ECOLOGY
Shubhangi Chaturvedi, Chandravanshi Shubham Arun, Poornima Singh Thakur, Pritee Khanna, Aparajita Ojha
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引用次数: 0

摘要

森林是宝贵的资源,而火灾是森林生态系统不可或缺的自然过程。虽然火灾具有多种生态效益,但近来世界各地频繁发生的火灾却引起了人们的关注。这些火灾覆盖了数百万公顷的林地,造成了人员伤亡、野生栖息地、民用基础设施和环境的严重破坏。约 90% 的野外火灾是人类有意或无意造成的。及早发现靠近人类居住区和野生动物栖息地的火灾有助于减轻火灾危害。在过去的十年中,已经提出了许多基于人工智能的解决方案,这些方案优先检测火灾烟雾,因为通过遥感可以捕捉到火灾烟雾,并提供野外火灾的早期迹象。然而,这些方法大多计算量大或误报率高。本文提出了一种轻量级深度神经网络模型,用于在卫星或其他遥感来源捕获的图像中检测火灾烟雾。由卷积和视觉变换块组成的混合网络每秒仅需 60 万个参数和 4 亿次浮点运算,就能在正常和多雾环境条件下高效地检测烟雾。该模型在四个数据集上的表现优于七种最先进的方法,其中包括从 "中分辨率成像分光仪 "卫星图像中自行收集的数据集。该模型在三个数据集上的准确率超过 99%,在第四个数据集上的准确率达到 93.90%。该模型对提取的特征进行了 t 分布随机邻域嵌入,证明了其卓越的特征学习能力。值得注意的是,即使是仅占卫星图像面积 2% 的微小烟雾,该模型也能有效地检测出来。由于对内存和计算量的要求较低,所提出的模型效果非常好,因此适合部署在资源有限的设备中,用于森林监控和早期火灾烟雾检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ultra-lightweight convolution-transformer network for early fire smoke detection
Forests are invaluable resources, and fire is a natural process that is considered an integral part of the forest ecosystem. Although fire offers several ecological benefits, its frequent occurrence in different parts of the world has raised concerns in the recent past. Covering millions of hectares of forest land, these fire incidents have resulted in the loss of human lives, wild habitats, civil infrastructure, and severe damage to the environment. Around 90% of wildland fires have been caused by humans intentionally or unintentionally. Early detection of fire close to human settlements and wildlife centuries can help mitigate fire hazards. Numerous artificial intelligence-based solutions have been proposed in the past decade that prioritize the detection of fire smoke, as it can be caught through remote sensing and provide an early sign of wildland fire. However, most of these methods are either computationally intensive or suffer from a high false alarm rate. In this paper, a lightweight deep neural network model is proposed for fire smoke detection in images captured by satellites or other remote sensing sources. With only 0.6 million parameters and 0.4 billion floating point operations per second, the hybrid network of convolutional and vision transformer blocks efficiently detects smoke in normal and foggy environmental conditions. It outperforms seven state-of-the-art methods on four datasets, including a self-collected dataset from the “Moderate Resolution Imaging Spectroradiometer” satellite imagery. The model achieves an accuracy of more than 99% on three datasets and 93.90% on the fourth dataset. The t-distributed stochastic neighbor embedding of extracted features by the proposed model demonstrates its superior feature learning capabilities. It is remarkable that even a tiny occurrence of smoke covering just 2% of the satellite image area is efficiently detected by the model. With low memory and computational demands, the proposed model works exceedingly well, making it suitable for deployment in resource constrained devices for forest surveillance and early fire smoke detection.
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来源期刊
Fire Ecology
Fire Ecology ECOLOGY-FORESTRY
CiteScore
6.20
自引率
7.80%
发文量
24
审稿时长
20 weeks
期刊介绍: Fire Ecology is the international scientific journal supported by the Association for Fire Ecology. Fire Ecology publishes peer-reviewed articles on all ecological and management aspects relating to wildland fire. We welcome submissions on topics that include a broad range of research on the ecological relationships of fire to its environment, including, but not limited to: Ecology (physical and biological fire effects, fire regimes, etc.) Social science (geography, sociology, anthropology, etc.) Fuel Fire science and modeling Planning and risk management Law and policy Fire management Inter- or cross-disciplinary fire-related topics Technology transfer products.
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