UAV Landing Using Computer Vision Techniques for Human Detection The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRedxe2x80x94without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNetV2, and SSDLiteMobileNetV2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5xe2x80x9310 m, with recalls from 59%xe2x80x9376%. The results confirm that a low computing power and costeffective system can perform aerial human detection, estimating the landing position without an additional visual marker.

A visionbased system for autonomous vertical landing of unmanned aerial vehicles Over the last few years, different researchers have been developing protocols and applications in order to land unmanned aerial vehicles (UAVs) autonomously. However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications, such as package retrieval and delivery. Therefore, in this paper, we present a solution for high precision landing based on the use of ArUco markers. In our solution, a UAV equipped with a camera is able to detect ArUco markers from an altitude of 20 meters. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. We evaluated our proposal using our own UAV simulation platform (ArduSim), and validated it using real UAVs. The results show an average offset of only 11 centimeters, which vastly improves the landing accuracy compared to the traditional GPSbased landing, that typically deviates from the intended target by 1 to 3 meters.