Abstract
In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.
Highlights
Target detection has attracted significant attention for autonomous aerial vehicles due to its notable benefits and recent progress
Visual recognition systems in a unmanned aerial vehicle (UAV) can be used in many applications, like video surveillance, self-driving systems [6], a panoramic aerial view for traffic management, traffic surveillance, road conditions, and emergency response, which has been the interest of transportation departments for many years [2,7]
We present the experimental results from the aerial vehicle and the performance according to the systems we used in the aerial vehicle
Summary
Target detection has attracted significant attention for autonomous aerial vehicles due to its notable benefits and recent progress. Target tracking with an unmanned aerial vehicle (UAV) can be used for intelligence, surveillance, and reconnaissance missions [1]. Pedestrian detection [3], dynamic vehicle detection, and obstacle detection [4] can improve the features of the guiding assistance system. Object recognition technologies for self-driving vehicles have strict requirements in terms of accuracy, unambiguousness, robustness, space demand, and costs [5]. Object recognition and tracking features in an aerial vehicle can assist in drone navigation and obstacle avoidance. Visual recognition systems in a UAV can be used in many applications, like video surveillance, self-driving systems [6], a panoramic aerial view for traffic management, traffic surveillance, road conditions, and emergency response, which has been the interest of transportation departments for many years [2,7]
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