Evaluation Monocular Depth Estimation Model for UAVApplications

Safe and autonomous movement capabilities in unmanned aerial vehicles (UAVs) aredirectly dependent on accurate and reliable perception of the environment. Depthinformation presents a fundamental requirement for critical tasks such as obstacleavoidance, path planning, and autonomous navigation. Although lidar and stereocamera systems provide high accuracy, they are not always applicable in UAV applications due to limitations such as cost, weight, and energy consumption. Therefore,monocular methods that estimate depth from a single RGB image offer a strong alternative for UAVs due to their low hardware requirements. However, the vast majorityof existing monocular depth estimation algorithms in the literature have been developed for ground-level driving scenarios or indoor environments and have not beenadequately evaluated under UAV-specific perspectives and environmental conditions.This study comprehensively compares three prominent monocular depth estimationmodels (DepthAnything V2, DistillAnyDepth, and Marigold) in the current literature on both real and simulation-based datasets. The models were evaluated in termsof error and accuracy metrics, as well as computational costs. Experimental resultsshow that the DepthAnything and DistillAnyDepth models generally offer higheraccuracy and stable performance, with DistillAnyDepth being the most suitable solution for real-time UAV applications due to its low latency. This study provides animportant comparative reference in the field of monocular depth estimation in UAVs