Energy-Aware Multilingual Vision Language Models for Drone Smart Sensing
[EN] Drone-based smart sensing increasingly relies on Vision¿Language Models (VLMs) for real-time scene interpretation, obstacle detection, and autonomous navigation reasoning. Deploying such systems at scale demands not only high perceptual accuracy but also energy efficiency, a critical constraint...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::812e66af200f3c91792a0510e8e2c1b8 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/236065 |
| Access Level: | acceso abierto |
| Palabra clave: | Large language models Vision language models Drone smart sensing AI energy score Autonomous navigation UAV perception Scene understanding |
| Sumario: | [EN] Drone-based smart sensing increasingly relies on Vision¿Language Models (VLMs) for real-time scene interpretation, obstacle detection, and autonomous navigation reasoning. Deploying such systems at scale demands not only high perceptual accuracy but also energy efficiency, a critical constraint on battery-powered Unmanned Aerial Vehicle (UAV) platforms, and linguistic flexibility for multinational operational contexts. We present a systematic benchmarking framework that jointly evaluates perception performance and inference energy for five open-source VLMs across thirteen languages spanning six language families, including three low-resource varieties (Arabic, Basque, and Luxembourgish). Using imagery sampled from the Berkeley DeepDrive 10K (BDD10K), each model is evaluated on four sensing tasks of increasing difficulty scored via a sentence-transformer backbone, with energy measured following the AI Energy Score methodology (Wh per 1000 queries) through continuous NVML-based GPU power sampling. Across 65 language¿model observations, LLaVA-1.6 achieves the highest perception score (¿¿¿¿¿¿=0.160 ) while Phi-3-Vision attains the best energy efficiency (66.3 Wh/1000 queries); energy consumption and task accuracy are statistically uncorrelated (Spearman ¿=0.001 ; ¿=0.995 ). A formal UAV inference energy model instantiated for four commercial platforms confirms LLaVA-1.6 as Pareto-optimal on heavy-lift platforms (DJI Matrice 300/350 RTK) and LLaVA-1.5 on the energy-constrained Matrice 30; compact UAVs such as the Mavic 3 Enterprise exceed the budget of all evaluated models at standard query rates. Friedman tests reveal significant cross-language variability in energy demands (¿2=40.43 ; ¿=3.5×10¿8 ) and navigation reasoning performance (¿2=13.35 ; ¿=0.010 ). Critically, we document a double penalty for low-resource languages, which simultaneously incur higher inference energy costs and lower task accuracy, with direct implications for equitable multilingual UAV deployments. |
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