Drone imagery forest fire detection and classification using modified deep learning model

With the progression of information technologies, unmanned aerial vehicles (UAV) or drones are more significant in remote monitoring the environment. One main application of UAV technology relevant to nature monitoring is monitoring wild animals. Among several natural disasters, Wildfires are one of the deadliest and cause damage to millions of hectares of forest lands or resources which threatens the lives of animals and people. Drones present novel features and convenience which include rapid deployment, adjustable and wider viewpoints, less human intervention, and high maneuverability. With the effective enforcement of deep learning in many applications, it is used in the domain of forest fire recognition for enhancing the accuracy of forest fire detection through extraction of deep semantic features from images. This article concentrates on the design of the drone imagery forest fire detection and classification using modified deep learning (DIFFDC-MDL) model. The presented DIFFDC-MDL model aims in the detection and classification of forest fire in drone imagery. To accomplish this, the presented DIFFDC-MDL model designs a modified MobileNet-v2 model to generate feature vectors. For forest fire classification, a simple recurrent unit model is applied in this study. In order to further improve the classification outcomes, shuffled frog leap algorithm is used. The simulation outcome analysis of the DIFFDC-MDL system was tested utilizing a database comprising fire and non-fire samples. The extensive comparison study referred that the improvements of the DIFFDC-MDL system over other recent algorithms.