As applications of drone proliferate, it has become increasingly important to equip drones with automatic sense and avoid (SAA) algorithms to address safety and liability concerns. Sense and avoid algorithms can be based upon either active or passive sensing methods. Each of them has advantages when compared to the other but neither is sufficient by itself. Therefore, especially for application such as autonomous navigation where failure could be catastrophic, deploying both passive and active sensors simultaneously and utilizing inputs from them become critical to detect and avoid objects in a reliable way. As part of the solution, in this paper, we present an efficient SAA algorithm based on input from multiple stereo cameras, which can be implemented on a low-cost and low-power embedded processor. In this algorithm, we construct an instantaneous 3D occupancy grid (OG) map at each time instance using the disparity information from the stereo cameras. Then, we filter noise using spacial information, and further filter noise using a probabilistic approach based on temporal information. Using this OG Map, we detect threats to the drone in order to determine the best trajectory for it to reach a destination.
Object detection using aerial drone imagery has received a great deal of attention in recent years. While visible light images are adequate for detecting objects in most scenarios, thermal cameras can extend the capabilities of object detection to night-time or occluded objects. As such, RGB and Infrared (IR) fusion methods for object detection are useful and important. One of the biggest challenges in applying deep learning methods to RGB/IR object detection is the lack of available training data for drone IR imagery, especially at night. In this paper, we develop several strategies for creating synthetic IR images using the AIRSim simulation engine and CycleGAN. Furthermore, we utilize an illumination-aware fusion framework to fuse RGB and IR images for object detection on the ground. We characterize and test our methods for both simulated and actual data. Our solution is implemented on an NVIDIA Jetson Xavier running on an actual drone, requiring about 28 milliseconds of processing per RGB/IR image pair.
In the last years, the ductility and easiness of usage of unmanned aerial vehicles (UAV) and their affordable cost have increased the drones use by industry and private users. However, drones carry the potential of many illegal activities from smuggling illicit material, unauthorized reconnaissance and surveillance of targets and individuals, to electronic and kinetic attacks in the worse threatening scenarios. As a consequence, it has become important to develop effective and affordable coun- termeasures to report of a drone flying over critical areas. In this context, our research chooses different short term parametrization in time and frequency domain of environmental audio data to develop a machine learning based UAV warning system which employs the support vector machines to understand and recognize the drone audio fingerprint. Preliminary experimental results have shown the effectiveness of the proposed approach.
The rapid development of Unmanned Aerial Vehicle (UAV) technology, -also known as drones- has raised concerns on the safety of critical locations such as governmental buildings, nuclear stations, crowded places etc. Computer vision based approach for detecting these threats seems as a viable solution due to various advantages. We envision an autonomous drone detection and tracking system for the protection of strategic locations. It has been reported numerous times that, one of the main challenges for aerial object recognition with computer vision is discriminating birds from the targets. In this work, we have used 2-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features and classified targets as a drone or bird by a neural network. For the training of this system, a large dataset composed of birds and drones is gathered from open sources. We have achieved up to 85.3% overall correct classification rate.
Advancements in sensing, computing, image processing, and computer vision technologies are enabling unprecedented growth and interest in autonomous vehicles and intelligent machines, from self-driving cars to unmanned drones, to personal service robots. These new capabilities have the potential to fundamentally change the way people live, work, commute, and connect with each other, and will undoubtedly provoke entirely new applications and commercial opportunities for generations to come. The main focus of AVM is perception. This begins with sensing. While imaging continues to be an essential emphasis in all EI conferences, AVM also embraces other sensing modalities important to autonomous navigation, including radar, LiDAR, and time of flight. Realization of autonomous systems also includes purpose-built processors, e.g., ISPs, vision processors, DNN accelerators, as well core image processing and computer vision algorithms, system design and architecture, simulation, and image/video quality. AVM topics are at the intersection of these multi-disciplinary areas. AVM is the Perception Conference that bridges the imaging and vision communities, connecting the dots for the entire software and hardware stack for perception, helping people design globally optimized algorithms, processors, and systems for intelligent “eyes” for vehicles and machines.