Real-time small object detection with YOLOv8n/8s and YOLOv11n/11s models in complex natural landscapes

Unmanned Aerial Vehicles (UAVs) are increasingly employed for real-time object detection in critical applications such as security surveillance, disaster response, and environmental monitoring. However, accurate detection in UAV imagery remains challenging due to small target sizes, cluttered backgrounds, and varying environmental conditions. This study evaluates the performance of YOLOv8n/v8s and YOLOv11n/11s models for human detection in UAV-captured imagery across diverse natural landscapes. To ensure practical deployment in resource-constrained environments, the models were implemented on a Raspberry Pi 5 using the OpenVINO framework. Experimental results show that both YOLO series achieve comparable detection accuracy in the range of 80–82%, with YOLOv8n and YOLOv11n demonstrating the highest processing speeds of 10.50 and 11.04 frames per second (FPS), respectively. These findings confirm the feasibility of using lightweight YOLO models for real-time human detection on embedded systems. The results highlight the potential of integrating edge AI and UAVs for autonomous, on-site monitoring in remote or complex terrains, offering scalable solutions for intelligent aerial surveillance.