论文标题
通过Yolo轻巧的多无形无人机检测和3D-Localization
Lightweight Multi-Drone Detection and 3D-Localization via YOLO
论文作者
论文摘要
在这项工作中，我们介绍并评估一种使用最先进的Yolov4对象检测算法和立体声三角测量的方法，以执行实时多个无人机检测和三维定位。我们的计算机视觉方法消除了对计算昂贵的立体声匹配算法的需求，从而大大降低了内存足迹并使其可在嵌入式系统上部署。我们的无人机检测系统是高度模块化的（支持各种检测算法），并且能够识别系统中的多个无人机，实时检测准确性高达77 \％，平均fps为332（在NVIDIA TITAN XP上）。我们还测试了AirSim环境中的完整管道，以最大8米的距离检测无人机，平均误差为$ 23 \％$ $。我们还使用预先训练的模型和策划的合成立体声数据集发布了该项目的源代码。
In this work, we present and evaluate a method to perform real-time multiple drone detection and three-dimensional localization using state-of-the-art tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer vision approach eliminates the need for computationally expensive stereo matching algorithms, thereby significantly reducing the memory footprint and making it deployable on embedded systems. Our drone detection system is highly modular (with support for various detection algorithms) and capable of identifying multiple drones in a system, with real-time detection accuracy of up to 77\% with an average FPS of 332 (on Nvidia Titan Xp). We also test the complete pipeline in AirSim environment, detecting drones at a maximum distance of 8 meters, with a mean error of $23\%$ of the distance. We also release the source code for the project, with pre-trained models and the curated synthetic stereo dataset.
