Obstacle Detection for BVLOS Drones
This work addresses safety for autonomous drones under new EU regulations, but it is incremental as it applies existing methods to a specific domain.
The paper tackles obstacle detection for BVLOS drones to enable fail-safe landings, using deep learning object detection methods like YOLOv3 and YOLOv5, and concludes that while promising, more data is needed for real-life applications.
With the introduction of new regulations in the European Union, the future of Beyond Visual Line Of Sight (BVLOS) drones is set to bloom. This led to the creation of the theBEAST project, which aims to create an autonomous security drone, with focus on those regulations and on safety. This technical paper describes the first steps of a module within this project, which revolves around detecting obstacles so they can be avoided in a fail-safe landing. A deep learning powered object detection method is the subject of our research, and various experiments are held to maximize its performance, such as comparing various data augmentation techniques or YOLOv3 and YOLOv5. According to the results of the experiments, we conclude that although object detection is a promising approach to resolve this problem, more volume of data is required for potential usage in a real-life application.