Computer Vision for Fire Detection on UAVs-From Software to Hardware

Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.

Automated summary

The risk of fire hazard can have catastrophic consequences, but artificial intelligence combined with UAVs has been able to identify and avoid this risk through computer vision. Research has shown that multi-copters are the most common type of vehicle used, with the combination of RGB and thermal cameras being prevalent in most applications. The increasing trend in using Convolutional Neural Networks (CNNs) indicates a shift towards more advanced technology in fire detection and prevention.