TY - JOUR
TI - Drone imagery forest fire detection and classification using modified deep learning model
AU - Mashraqi Aisha M
AU - Asiri Yousef
AU - Algarni Abeer D
AU - Abu-Zinadah Hanaa
JN - Thermal Science
PY - 2022
VL - 26
IS - 101
SP - 411
EP - 423
PT - Article
AB - 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.