The number of unmanned aerial vehicles (UAVs, also known as drones) has increased dramatically in the airspace worldwide for tasks such as surveillance, reconnaissance, shipping and delivery. However, a small number of them, acting maliciously, can raise many security risks. Recent Artificial Intelligence (AI) capabilities for object detection can be very useful for the identification and classification of drones flying in the airspace and, in particular, are a good solution against malicious drones. A number of counter-drone solutions are being developed, but the cost of drone detection ground systems can also be very high, depending on the number of sensors deployed and powerful fusion algorithms. We propose a low-cost counter-drone solution composed uniquely by a guard-drone that should be able to detect, locate and eliminate any malicious drone. In this paper, a state-of-the-art object detection algorithm is used to train the system to detect drones. Three existing object detection models are improved by transfer learning and tested for real-time drone detection. Training is done with a new dataset of drone images, constructed automatically from a very realistic flight simulator. While flying, the guard-drone captures random images of the area, while at the same time, a malicious drone is flying too. The drone images are auto-labelled using the location and attitude information available in the simulator for both drones. The world coordinates for the malicious drone position must then be projected into image pixel coordinates. The training and test results show a minimum accuracy improvement of 22% with respect to state-of-the-art object detection models, representing promising results that enable a step towards the construction of a fully autonomous counter-drone system.

中文翻译：

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改进反无人机系统的实时无人机检测

用于监视、侦察、运输和交付等任务的无人飞行器（UAV，也称为无人驾驶飞机）的数量在全球空域急剧增加。但是，其中少数恶意行为可能会引发许多安全风险。最近用于物体检测的人工智能 (AI) 功能对于识别和分类在空域中飞行的无人机非常有用，尤其是对抗恶意无人机的良好解决方案。许多反无人机解决方案正在开发中，但无人机探测地面系统的成本也可能非常高，这取决于部署的传感器数量和强大的融合算法。我们提出了一种低成本的反无人机解决方案，该解决方案由应该能够检测到的警卫无人机组成，定位并消除任何恶意无人机。在本文中，使用最先进的对象检测算法来训练系统检测无人机。通过迁移学习改进了三个现有的对象检测模型，并针对实时无人机检测进行了测试。训练是使用一个新的无人机图像数据集完成的，该数据集由一个非常逼真的飞行模拟器自动构建。在飞行时，警卫无人机会随机捕捉该区域的图像，而与此同时，恶意无人机也在飞行。无人机图像使用模拟器中可用的位置和姿态信息自动标记，用于两架无人机。然后必须将恶意无人机位置的世界坐标投影到图像像素坐标中。