Development of a Forest Fire Detection System Using a Drone-based Convolutional Neural Network Model

Abstract

Considering forest fires cause environmental destruction, ecosystem collapse, and severe damage to human lives and nature, developing a real-time, accurate, and stable forest fire detection system has become a critical issue in modern society. In this study, a drone-based forest fire detection system was developed using a convolutional neural network (CNN) model. Real-time forest fire detection models were developed using the CNN-based MobileNet algorithm, and their fire detection performance was evaluated. The main research results indicated that errors decreased and accuracy tended to increase during the model training and validation process as training progressed. Moreover, the V1 model exhibited the highest validation accuracy of 0.9466 among the MobileNet V1, V2, and V3 models and showed the highest accuracy of 0.9667 in evaluating the new test dataset during the model evaluation process.