Lightweight Multi-Drone Detection and 3D-Localization via YOLO
This addresses drone detection for security or monitoring applications, but it is incremental as it adapts existing methods to a specific domain.
The paper tackles real-time multiple drone detection and 3D localization using tiny-YOLOv4 and stereo triangulation, achieving 77% accuracy at 332 FPS on an Nvidia Titan Xp and a mean error of 23% of distance in simulations.
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.