People Detection in small-scale UAV Aerial Images
In the last years, the technology of small-scale Unmanned Aerial Vehicles (UAVs) has steadily improved in terms of flight time, automatic control, and image acquisition. This has led to the development of several applications for low-altitude tasks, such as vehicle tracking, person identification, and object recognition. UAVs are usually equipped with heterogeneous sensors such as thermal cameras and Red Green Blue (RGB) cameras. The latter are the most used for three reasons. The first is that the acquired images are easy to interpret by human eye. The second is their cost, which is more affordable with respect to other sensors. The third is the availability, since also the commercial UAVs mount an RGB sensor. Moreover, recent developments in fields such as Deep Learning (DL) have improved the autonomous capabilities and the precision of the UAVs in tasks such as guidance. Despite this, there are no algorithms optimized for UAVs for object detection task. This project is focused on the latter. In detail, the aim is to propose a modified deep neural network optimized for the detection of people in aerial images acquired at very low altitudes by small-scales UAVs.