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

TL;DR

This paper introduces a low-cost drone equipped with machine learning for early forest fire detection, aiming to improve accuracy and reduce false alarms through visual smoke recognition and additional supervision.

Contribution

It presents a novel integrated system combining drone-based visual detection with deep learning and supervision to enhance early fire detection accuracy.

Findings

High detection accuracy demonstrated in experiments

Reduced false alarm rate compared to existing methods

Effective in both simulation and real-world environments

Abstract

This paper proposes a new machine learning based system for forest fire earlier detection in a low-cost and accurate manner. Accordingly, it is aimed to bring a new and definite perspective to visual detection in forest fires. A drone is constructed for this purpose. The microcontroller in the system has been programmed by training with deep learning methods, and the unmanned aerial vehicle has been given the ability to recognize the smoke, the earliest sign of fire detection. The common problem in the prevalent algorithms used in fire detection is 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. Due to the mobile vision ability of the unmanned aerial vehicle, the data can be controlled from any point of view clearly and continuously. System performance are validated by conducting experiments in both simulation and physical environments.