Machine Learning Based Early Fire Detection System using a Low-Cost Drone

In this period where the number of systems developed by utilizing unmanned aerial technology is increasing day by day, unmanned aerial vehicles will be used to achieve the targets of minimizing the destruction of our forests which are the lungs of the world and optimizing the usage of workforce and time resources [1, 2, 3]. As a result of the application carried out in line with the subject of this paper, it is proposed that the system based on the detection of smoke image with unmanned aerial vehicle can provide a great benefit in reducing the error rate occurring in fire detection. The microprocessor in the system has been trained with deep learning methods and has been given the ability to recognize smoke image, which is the earliest sign of fire diagnosis. The most fundamental problem in the common algorithms used in fire detection is the high level of false alarm and overlook rate [4,5].Confirming the result obtained from the detection and defining an additional proof will increase the reliability of the system as well as the accuracy. Since the drones provide a mobile vision, the point of view can be controlled by the ground station can manipulate it for the sake of the accuracy of the result. The application developed in line with the subject of the paper was implemented in both simulation and physical environments and the advantages of early fire detection system and analysis results are discussed in the conclusion section of the article.