# Computer Vision for Fire Detection on UAVs--From Software to Hardware
## 5 Discussion  
Fires are catastrophic events that are constantly being studied and researched. As a result, more effective ways are being discovered to prevent them. The present review, which was carried out in order to record all the above used methods, techniques, software and hardware, presents a complete set of possible solutions for future researchers. Initially, the most used UAV models were drones. Such vehicles, in addition to their mechanical advantages, such as weight, vertical takeoff and landing have also the tremendous ability for fast maneuvers and high integration of the various components and materials. Furthermore, drones can also hover. Through this state of flight, the vehicle can be stationary at one point and monitor a specific area. This increases its flying and taking-images capabilities. Of course, for such a case, due to their structure, these vehicles can easily integrate more than one camera with a more evenly distributed weight, compared to other types of vehicles. In terms of cameras, the RGB, with various resolutions and architecture, alongside thermal cameras have key positions in this type of applications. Through thermal cameras, a sharp change in temperature can be detected, even if there are visual obstructions, which makes it easier to detect fires [67]. A very important role, of course, is the ability of the vehicle to send information about the location via GPS. Many implementations have used this module as well as various others such as the use of GNSS or IMUs. Without this kind of technique, it would not be possible to identify the vehicle's location and position. In this case, a real-world fire detection application will be useless and such an implementation would be only for research purposes. The fire detection is done through AI models. The development of new artificial intelligence models leads to applications with high accuracy. More specifically, the Convolutional Neural Networks (CNNs) have been applied to more and more Computer Vision applications. Convolutional Neural Networks gradually replace the classic Computer Vision algorithms, due to their great accuracy results and their ease of implementation. For this reason, CNNs, such as Faster CNN, YOLO v3 and VGG-16, have the largest number of applications compared to classical Computer Vision algorithms or even other artificial intelligence models, such as neural networks or fuzzy systems and Support Vector Machines (SVM). In addition, the increased interest in UAVs and Computer Vision leads to constantly improved versions of platforms and software which are used more and more in applications related to this type of implementation. Some of them aim at the construction, the mapping of the environment of the UAV or its path planning. Furthermore, others have been developed to achieve higher accuracy of the models or algorithms as well as to improve the quality of the images and the data transmission capabilities. The constant development in terms of hardware and software, shows that the interest of researchers and engineers remains unwavering and the need to achieve greater accuracy in applications where UAVs and Computer Vision are used. The used datasets are always one of the most important elements of the training process of an AI model. It has been observed that there are datasets consisting of a large number of diverse photographs, which concerns the fires themselves as well as the image filters and their environment. As already mentioned, CNNs are constantly evolving and being implemented in more and more implementations. This leads to the construction and