Autonomous environmental protection drone - Saraiva, Carlos Miguel Domingues (2021)
During the summer, forest fires are the main reason for deforestation and the damage caused to homes and property in different communities around the world. The use of Unmanned Aerial Vehicles (UAVs, and also known as drones) applications has increased in recent years, making them an excellent solution for difficult tasks such as wildlife conservation and forest fire prevention. A forest fire detection system can be an answer to these tasks. Using a visual camera and a Convolutional Neural Network (CNN) for image processing with an UAV can result in an efficient fire detection system. However, in order to be able to have a fully autonomous system, without human intervention, for 24-hour fire observation and detection in a given geographical area, it requires a platform and automatic recharging procedures. This dissertation combines the use of technologies such as CNNs, Real Time Kinematics (RTK) and Wireless Power Transfer (WPT) with an on-board computer and software, resulting in a fully automated system to make forest surveillance more efficient and, in doing so, reallocating human resources to other locations where they are most needed.