论文标题
基于图像的野火的早期检测系统
Image-based Early Detection System for Wildfires
论文作者
论文摘要
野火是一种灾难性的现象,会损害土地,财产损失,空气污染甚至人类生命的丧失。由于气候变化造成的温暖和干燥条件,预计未来几年将发生更严重和不可控制的野火。这可能导致全球野火危机,并对我们的星球产生可怕的后果。因此,必须使用技术来帮助防止野火传播。防止野火变得太大之前的一种方法是进行早期检测,即在实际火灾开始之前检测烟雾。在本文中,我们介绍了使用机器学习的野火检测和警报系统,以高度准确性地检测野火烟雾,并可以向用户发送即时警报。目前,我们的技术在美国正在使用每天监视数百台相机的数据。我们表明,我们的系统具有很高的真实检测率和低的错误检测率。我们的绩效评估研究还表明,平均而言,我们的系统比实际人更快地检测到野火烟雾。
Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person.