Automatic Forest Fire Detection Based on a Machine Learning and Image Analysis Pipeline

Abstract

Forest fires can have devastating consequences if not detected and fought before they spread. This paper presents an automatic fire detection system designed to identify forest fires, preferably, in their early stages. The system pipeline processes images of the forest environment and is able to detect the presence of smoke or flames. Additionally, the system is able to produce an estimation of the area under ignition so that its size can be evaluated. In the process of classification of a fire image, one Deep Convolutional Neural Network was used to extract, from the images, the descriptors which are then applied to a Logistic Regression classifier. At a later stage of the pipeline, image analysis and processing techniques at color level were applied to assess the area under ignition. In order to better understand the influence of specific image features in the classification task, the organized dataset, composed by 882 images, was associated with relevant image metadata (eg presence of flames, smoke, fog, clouds, human elements). In the tests, the system obtained a classification accuracy of 94.1% in 695 images of daytime scenarios and 94.8% in 187 images of nighttime scenarios. It presents good accuracy in estimating the flame area when compared with other approaches in the literature, substantially reducing the number of false positives and nearly keeping the same false negatives stats.