UAV sensor data applications with deep neural networks: A comprehensive survey

Abstract

The use of Unmanned Aerial Vehicles (UAVs) has become increasingly popular in recent years, leading to a surge in research on this topic that is widely represented in the literature. They would be examined in accordance with the requirements of the sensors they house, their operating limits, and the output information they give. Since perception, planning, localization, and control are the major tasks of UAVs, an outstanding problem solver Deep learningis recently used in these systems. It is ideally suited for UAV applications because of its great capacity for learning representations from the complicated data received in actual situations. This paper provides an extensive review and in-depth analysis of current advancements in UAVs that have applications with DNN, as well as a quick summary of research and development during the previous ten years. In terms of UAVs flight monitoring, remote sensing and vision capability, and energy modeling, this survey provides a roadmap for understanding the sequential development of sophisticated UAVs by reviewing 173 retrieved papers. For this purpose, first of all, the main titles of the studies carried out for UAV applications were gathered under a taxonomy, and then information about the studies carried out in recent years related to these main research fields was given. In the last part of the study, attention was drawn to the still challenging points, such as autonomous fault detection, path planning, and onboard event detection, which are anticipated to be the future trends involving UAV applications.