ABSTRACT
This chapter offers a new method to detect forest fires earlier and more accurately. It aims to bring a new and definite perspective to visual detection in forest fires. The micro controller in the system has been programmed by training with deep learning methods, and the unmanned aerial vehicle has been given the ability to recognize smoke as the earliest sign of fire diagnosis. Common problems in the prevalent algorithms used in fire detection are the high false alarm and overlook rates. Confirming the result obtained from the visualization with an additional supervision stage will increase the reliability of the system as well as guarantee the accuracy of the result. Thanks to the mobile vision of the unmanned aerial vehicle, the data can be controlled from any point of view clearly and continuously. An application developed in accordance with the subject of the chapter was realized in the simulation environment, and the advantages of an early fire detection system with the results of the analysis are discussed in the conclusion of the chapter.
