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.

翻译：随着欧盟引入新条例,未来视力线以外无人驾驶飞机(BVLOS)即将开花,这导致创建了旨在创建自主安全无人驾驶飞机的BEARTS项目,重点是这些条例和安全。本技术文件描述了该项目中一个模块的第一步,该模块围绕探测障碍以避免在无故障着陆时发生障碍。我们研究的主题是深层学习动力天体探测方法,并进行了各种实验,以最大限度地提高其性能,例如比较各种数据增强技术或YOLOv3和YOLOv5。根据实验结果,我们的结论是,虽然物体探测是解决这一问题的一个很有希望的方法,但在实际应用中可能使用的数据需要更多。