Forest fire detection in aerial vehicle videos using a deep ensemble neural network model

Abstract

Purpose The purpose of this paper is to present a deep ensemble neural network model for the detection of forest fires in aerial vehicle videos. Design/methodology/approach Presented deep ensemble models include four convolutional neural networks (CNNs): a faster region-based CNN (Faster R-CNN), a simple one-stage object detector (RetinaNet) and two different versions of the you only look once (Yolo) models. The presented method generates its output by fusing the outputs of these different deep learning (DL) models. Findings The presented fusing approach significantly improves the detection accuracy of fire incidents in the input data. Research limitations/implications The computational complexity of the proposed method which is based on combining four different DL models is relatively higher than that of using each of these models individually. On the other hand, however, the performance of the proposed approach is considerably higher than that of any of the four DL models. Practical implications The simulation results show that using an ensemble model is quite useful for the precise detection of forest fires in real time through aerial vehicle videos or images. Social implications By this method, forest fires can be detected more efficiently and precisely. Because forests are crucial breathing resources of the earth and a shelter for many living creatures, the social impact of the method can be considered to be very high. Originality/value This study fuses the outputs of different DL models into an ensemble model. Hence, the ensemble model provides more potent and beneficial results than any of the single models.