A Novel Framework for Early Fire Detection Using Terrestrial and Aerial 360-Degree Images

Abstract

In this paper, in order to contribute to the protection of the value and potential of forest ecosystems and global forest future we propose a novel fire detection framework, which combines recently introduced 360-degree remote sensing technology, multidimensional texture analysis and deep convolutional neural networks. Once 360-degree data are obtained, we convert the distorted 360-degree equirectangular projection format images to cubemap images. Subsequently, we divide the extracted cubemap images into blocks using two different sizes. This allows us to apply h-LDS multidimensional spatial texture analysis to larger size blocks and then, depending on the probability of fire existence, to smaller size blocks. Thus, we aim to accurately identify the candidate fire regions and simultaneously to reduce the computational time. Finally, the candidate fire regions are fed into a CNN network in order to distinguish between fire-coloured objects and fire. For evaluating the performance of the proposed framework, a dataset, namely “360-FIRE”, consisting of 100 images with unlimited field of view that contain synthetic fire, was created. Experimental results demonstrate the potential of the proposed framework.