Optimal Flight Speed and Height Parameters for Computer Vision Detection in UAV Search

Abstract

Unmanned Aerial Vehicles (UAVs) equipped with onboard cameras and deep-learning-based object detection algorithms are increasingly used in search operations. This study investigates the optimal flight parameters, specifically flight speed and ground sampling distance (GSD), to maximize a search efficiency metric called effective coverage. A custom dataset of 4468 aerial images with 35,410 annotated cardboard targets was collected and used to evaluate the influence of flight conditions on detection accuracy. The effects of flight speed and GSD were analyzed using regression modeling, revealing a trade-off between the area coverage and detection confidence of trained YOLOv8 and YOLOv11 models. Area coverage was modeled based on flight speed and camera specifications, enabling an estimation of the effective coverage. The results provide insights into how the detection performance varies across different operating conditions and demonstrate that a balance point exists where the combination of the detection reliability and coverage efficiency is optimized. Our table of the optimal flight regimes and metrics for the most commonly used cameras in UAV operations offers practical guidelines for efficient and reliable mission planning.