Real-Time Object Detection for Autonomous Drone Navigation Using YOLOv8
Abstract
This paper presents an original implementation and evaluation of YOLOv8-based real-time object detection for autonomous drone navigation. While previous works have primarily focused on earlier YOLO versions or simulation environments, this study deploys and benchmarks multiple YOLOv8 variants (YOLOv8n to YOLOv8x) on real-world, drone-compatible embedded platforms, such as the NVIDIA Jetson AGX Xavier, and Xavier NX. We propose an optimized object detection pipeline integrated with autonomous navigation algorithms, which have been tested in various environments and weather conditions. YOLOv8s demonstrated the best trade-off between detection accuracy (mAP@0.5 = 89.3%) and real-time inference speed (45.2 FPS) on Jetson AGX Xavier. The system consistently achieved over 90% successful autonomous flight performance in urban, rural, and mixed scenarios. These results confirm the feasibility of YOLOv8s for reliable real-time object detection in autonomous UAVs, paving the way for robust deployment in real-world applications.
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