Commercial Unmanned aerial vehicle (UAV) industry, which is publicly known as drone, has seen a tremendous increase in last few years, making these devices highly accessible to public. This phenomenon has immediately raised security concerns due to fact that these devices can intentionally or unintentionally cause serious hazards. In order to protect critical locations, the academia and industry have proposed several solutions in recent years. Computer vision is extensively used to detect drones autonomously compared to other proposed solutions such as RADAR, acoustics and RF signal analysis thanks to its robustness. Among these computer vision-based approaches, we see the preference of deep learning algorithms thanks to their effectiveness. In this paper, we are presenting an autonomous drone detection and tracking system which uses a static wide-angle camera and a lower-angle camera mounted on a rotating turret. In order to use memory and time efficiently, we propose a combined multi-frame deep learning detection technique, where the frame coming from the zoomed camera on the turret is overlaid on the wide-angle static camera’s frame. With this approach, we are able to build an efficient pipeline where the initial detection of small sized aerial intruders on the main image plane and their detection on the zoomed image plane is performed simultaneously, minimizing the cost of resource exhaustive detection algorithm. In addition to this, we present the integral system including tracking algorithms, deep learning classification architectures and the protocols.

中文翻译：

---

基于深度学习的策略，使用多个摄像机检测和跟踪无人机

商用无人机行业（俗称无人机）在过去几年中取得了巨大的增长，使这些设备非常容易为公众所用。由于这些设备可能有意或无意引起严重危害，因此这一现象立即引起了安全方面的关注。为了保护关键位置，近年来，学术界和工业界已经提出了几种解决方案。与其他提出的解决方案（如RADAR，声学和RF信号分析）相比，计算机视觉由于其强大的功能而被广泛用于自主检测无人机。在这些基于计算机视觉的方法中，由于深度学习算法的有效性，我们看到了它们的偏爱。在本文中，我们正在介绍一种自主的无人机检测和跟踪系统，该系统使用静态广角摄像头和安装在旋转炮塔上的低角度摄像头。为了有效地利用内存和时间，我们提出了一种组合式多帧深度学习检测技术，其中将来自转塔上的变焦摄像机的帧叠加在广角静态摄像机的帧上。通过这种方法，我们能够建立一条有效的管道，在该管道中，同时执行主图像平面上的小型空中入侵者的初始检测和缩放图像平面上的小型空中入侵者的检测，从而将资源穷举检测算法的成本降至最低。除此之外，我们介绍了包括跟踪算法，深度学习分类架构和协议在内的完整系统。