Classification of forest fire images using deep learning algorithms

Abstract

Deep learning techniques have emerged as one of most significant and advanced technologies in field of artificial intelligence over past two decades. These techniques demonstrate exceptional capabilities in analyzing complex data, with one of promising applications being accurate and effective wildfire detection in images. Deep learning algorithms serve as powerful and efficient means for image classification, including classification of forest fire images. These algorithms constitute an important part of field of image analysis and computer vision, as they can handle high-dimensional data and recognize complex patterns in images. Deep learning algorithms are used in classifying forest fire images to accurately identify and classify fire-affected areas, enabling prompt and effective actions to mitigate fire damage and protect environment and properties. It is worth noting that deep learning algorithms require large amounts of training data to achieve optimal performance, in addition to advanced technology for efficiently processing and analyzing images. By using deep learning algorithms in classifying forest fire images, classification accuracy and identification of fire-affected areas can be improved, allowing for effective allocation of efforts and resources to deal with fires and reduce resulting damages. In this study, three deep learning algorithms were employed: Deep Convolutional Neural Network (DCNN), VGG16 model, and MobileNet model, for detecting forest fires in images, with MobileNet achieving highest accuracy rate of 100%.