基于二维卷积神经网络和时域分析的火灾探测

P. A. Venâncio, T. M. Rezende, A. C. Lisboa, A. V. Barbosa
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引用次数: 4

摘要

在过去的几年里,深度学习的成功有了实质性的增加,特别是在计算机视觉任务的卷积神经网络方面。这些架构被广泛用于需要快速准确响应的紧急情况。在环境监测方面,有几项工作侧重于火灾探测,因为火灾越来越多地与诸如呼吸道疾病、经济损失和自然资源破坏等负面后果联系在一起。然而,烟雾和火灾的自动检测对计算机视觉系统提出了一个特别困难的挑战,因为这些物体的形状、颜色和纹理的可变性使得学习如何检测它们的过程比其他普通物体要复杂得多。因此,误报的数量可能会增加,这对于动员人力灭火的实时应用程序来说尤其成问题。这项工作提出了一个基于二维深度卷积网络的强大的火灾检测工具,能够抑制来自云、雾、汽车灯和其他容易与火灾和烟雾混淆的物体的假警报。我们的方法集成了目标检测器和目标跟踪器;这使得分析对象的时间行为并在决策过程中使用该信息成为可能。我们还提供了D-Fire,这是一个包含超过21,000张图像的公共标记数据集,用于训练和测试所提出的系统。实验结果表明,检测器达到mAP@0.50 = 75.91%,并且时间上下文的结合使假阳性率降低了60%,而真阳性率降低了2.86%。此外,该方法对火灾探测器增加的计算成本可以忽略不计,因此实时探测仍然是完全可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fire Detection based on a Two-Dimensional Convolutional Neural Network and Temporal Analysis
In the last few years there has been a substantial increase in the success of deep learning, especially with regard to convolutional neural networks for computer vision tasks. These architectures are being widely used in emergency situations, where a fast and accurate response is needed. In environmental monitoring, several works have focused on fire detection, since fires have been increasingly associated with negative consequences such as respiratory diseases, economical losses and the destruction of natural resources. The automatic detection of smoke and fire, however, poses a particularly difficult challenge to computer vision systems, since the variability in the shape, color and texture of these objects makes the process of learning how to detect them much more complicated than for other ordinary objects. As a consequence, the number of false positives may grow high, which is especially problematic for a real-time application that mobilizes human efforts to fight fire. This work presents a robust fire detection tool based on a 2D deep convolutional network capable of suppressing false alarms from clouds, fogs, car lights and other objects that are easily confused with fire and smoke. Our approach integrates an object detector with an object tracker; this makes it possible to analyze the temporal behavior of the object and use that information in the decision process. We also present D-Fire, a public and labeled dataset containing more than 21,000 images, which is used to train and test the proposed system. The experimental results show that the detector reached an mAP@0.50 = 75.91% and that the incorporation of the temporal context resulted in a 60% reduction in the false positive rate at the cost of a 2.86% reduction in true positive rate. In addition, the computational cost added by the proposed approach to the fire detector is negligible, so that real-time detection is still completely feasible.
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