A Real-Time Deep UAV Detection Framework Based on a YOLOv8 Perception Module

Unmanned Aerial Vehicles (UAVs) or drones are currently gaining a lot of popularity due to the versatility of this technology and its ability to perform multiple tasks in various industries. However, arbitrary or malicious use of drones can pose a major risk for public and aviation safety. The automated detection and neutralization of malicious drones to avoid fatal incidents is therefore of primary interest for aerial security systems. Recently, deep learning based approaches for object detection have gained great attention due to their high prediction accuracy. In the proposed work, we use a deep learning object detection model based on latest versions of YOLO, i.e. v7 and v8, to detect and track one drone by another drone in a pursuit-evasion scenario. The detection accuracy achieved with the YOLOv8 model is 99% Average Precision (AP) and the inference is 107.5 FPS, proving the effectiveness of the proposed approach for real-time UAV detection. A comparative study shows that the YOLOv7 model achieves the same accuracy but with slower inference.