Abstract
The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.
Highlights
Unmanned Aerial Vehicles (UAVs), called Unmanned Aircraft Systems (UASs), and commonly known as drones, are aircraft that fly without a pilot on-board
To the best of our knowledge, we present the first work that classifies object recognition methods for the case of UAVs that considers different heights intervals, whose definition is given by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints
UAVs achieved an unprecedented level of growth in many civil and military application domains, and computer vision has undoubtedly a key role in providing the necessary information concerning what is sensed
Summary
Unmanned Aerial Vehicles (UAVs), called Unmanned Aircraft Systems (UASs), and commonly known as drones, are aircraft that fly without a pilot on-board. They focus on high-level operations [6,9], they consider only a specific altitude and/or imaging type [10,11], a technology [12], or a precise use-case [5,13,14,15,16] It can be observed how, in these very important surveys, the computer vision point of view has been only partially considered. To the best of our knowledge, we present the first work that classifies object recognition methods for the case of UAVs that considers different heights intervals, whose definition is given by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints. The manuscript is organized as follows: first of all, the role of object detection from UAVs in terms of higher-level operations is illustrated, introducing some important sensors employed in the state of the art besides RGB cameras.
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