Instituto de Sistemas e Robótica, Instituto Superior Tecnico, University of Lisbon, 1049-001 Lisbon, Portugal.
Sensors (Basel). 2022 Feb 22;22(5):1701. doi: 10.3390/s22051701.
Autonomous systems can help firefighting operations by detecting and locating the fire spot from surveillance images and videos. Similar to many other areas of computer vision, Convolutional Neural Networks (CNNs) have achieved state-of-the-art results for fire and smoke detection and segmentation. In practice, input images to a CNN are usually downsized to fit into the network to avoid computational complexities and restricted memory problems. Although in many applications downsizing is not an issue, in the early phases of fire ignitions downsizing may eliminate the fire regions since the incident regions are small. In this paper, we propose a novel method to segment fire and smoke regions in high resolution images based on a multi-resolution iterative quad-tree search algorithm , which manages the application of classification and segmentation CNNs to focus the attention on informative parts of the image. The proposed method is more computationally efficient compared to processing the whole high resolution input, and contains parameters that can be tuned based on the needed scale precision. The results show that the proposed method is capable of detecting and segmenting fire and smoke with higher accuracy and is useful for segmenting small regions of incident in high resolution aerial images in a computationally efficient way.
自主系统可以通过从监控图像和视频中检测和定位火灾点来帮助消防作业。与计算机视觉的许多其他领域类似，卷积神经网络 (CNN) 在火灾和烟雾检测和分割方面取得了最先进的成果。在实践中，CNN 的输入图像通常会缩小尺寸以适应网络，以避免计算复杂性和受限的内存问题。尽管在许多应用中缩小尺寸不是问题，但在火灾点火的早期阶段，缩小尺寸可能会消除火灾区域，因为事件区域较小。在本文中，我们提出了一种新的方法，基于多分辨率迭代四叉树搜索算法来分割高分辨率图像中的火灾和烟雾区域，该算法管理分类和分割 CNN 的应用，将注意力集中在图像的信息部分上。与处理整个高分辨率输入相比，所提出的方法在计算上更有效率，并且包含可以根据所需的尺度精度进行调整的参数。结果表明，所提出的方法能够以更高的精度检测和分割火灾和烟雾，并且对于以计算有效的方式分割高分辨率航空图像中的小事件区域很有用。
