The investigation of the feasibility of using the YOLO (You Only Look Once) architecture for object detection in infrared images from unmanned aerial vehicles (UAVs) on low-power devices, specifically the Raspberry Pi, and Orange Pi, is conducted. The study measures the consumption of computing resources for each device, such as inference time (ms), peak power consumption (W), memory consumption (MB), inference energy (J), memory consumption (MB), and storage consumption (MB). It also investigates the correlation between number of model parameters and resource consumption of the different YOLO model sizes. Finally, the study draws conclusions about the expediency and realism of using YOLO on low-power devices for Edge Intelligence and proposes methods of speeding up work. The results show that YOLO can be used effectively on low-power devices with some optimizations to increase performance and energy efficiency.

Sparse visual odometry (VO) is a core component of lightweight unmanned aerial vehicle (UAV) visual navigation, yet the isolated effect of adding aligned metric depth to a minimal frame-to-frame pipeline is easily obscured in full SLAM systems. This paper presents a UAV-relevant controlled synthetic ablation of RGB-only and RGB-D geometric backends under fixed sparse frontends. ORB matching and KLT tracking are evaluated on a 32-sequence TartanAir validation split of flight-like synthetic RGB-D scenes by routing identical 2D correspondences either to Essential Matrix estimation or, with aligned depth, to PnP with RANSAC. The study reports ATE, Sim(3)-aligned ATE, translational and rotational RPE, robustness under temporal subsampling and RGB degradations, and isolated solver latency on a workstation and Raspberry Pi 4. At stride 1, RGB-D PnP reduces ATE by 61.8% for KLT and 29.1% for ORB, with translational RPE reductions of 61.6% and 41.9%. Rotational RPE reductions are stronger and persist across all tested strides, reaching 85.9% for KLT and 77.3% for ORB at stride 1. Sim(3) analysis shows that only 7–16% of PnP ATE is metric-scale drift. At coarser strides, however, KLT-PnP no longer improves ATE, showing that depth assistance depends on stable frontend tracking and valid depth-supported correspondences. The contribution is a reproducible diagnostic benchmark and failure-mode analysis for UAV-relevant depth-assisted sparse VO under oracle aligned depth, providing component-level evidence rather than full onboard deployment validation.