A Deep Learning Approach to Drone Monitoring

A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drone's location. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that, even being trained on synthetic data, the proposed system performs well on real world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public

Accuracy Assessment on Drone Measured Heights at Different Height Levels

The advancement in unmanned aerial system (UAS) technology has made it possible to attain an aerial unit, commonly known as a drone, at an affordable price with increasing precision and accuracy in positioning and photographing. While aerial photography is the most common use of a drone, many of the models available in the market are also capable of measuring height, the height of the drone above ground, or the altitude above the mean sea level. On board a drone, a barometer is used to control the flight height by detecting the atmospheric pressure change; while a GPS receiver is mainly used to determine the horizontal position of the drone. While both barometer and GPS are capable of measuring height, they are based on different algorithms. Our study goal was to assess the accuracy of height measurement by a drone, with different landing procedures and GPS settings

Drones and Cognitive Dissonance

There’s something about drones that makes sane people crazy. Is it those lean, futurist profiles? The activities drone technologies enable? Or perhaps it’s just the word itself–drone–a mindless, unpleasant, dissonant thrum. Whatever the cause, drones seem to produce an unusual kind of cognitive dissonance in many people. Some demonize drones, denouncing them for causing civilian deaths or enabling long-distance killing, even as they ignore the fact that the same (or worse) could be said of many other weapons delivery systems. Others glorify them as a low-cost way to “take out terrorists,” despite the strategic vacuum in which most drone strikes occur. Still others insist that US drone policy is just “business as usual,” despite the fact that these attacks may undermine US foreign policy goals while creating an array of new problems. It is worth taking a closer look at what is and is not new and noteworthy about drone technologies and the activities they enable. Ultimately, “drones” as such present few new issues—but the manner in which the US has been using them raises grave questions about their strategic efficacy and unintended consequences. In fact, the legal theories used to justify many US drone strikes risk dangerously hollowing out the rule of law itself

AlphaPilot: Autonomous Drone Racing

This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the 2019 AlphaPilot Challenge. Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. The global map and drift-compensated state estimate allow the drone to navigate through the race course even when the gates are not immediately visible and further enable to plan a near time-optimal path through the race course in real time based on approximate drone dynamics. The proposed system has been demonstrated to successfully guide the drone through tight race courses reaching speeds up to 8m/s and ranked second at the 2019 AlphaPilot Challenge.Comment: Accepted at Robotics: Science and Systems 2020, associated video at https://youtu.be/DGjwm5PZQT