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Semantic Scene Understanding with Large Language Models on Unmanned Aerial Vehicles by J. de Curtò; I. de Zarzà; Carlos T. Calafate is a Engineering article available to read on EtoBox.

What is Semantic Scene Understanding with Large Language Models on Unmanned Aerial Vehicles about?

Unmanned Aerial Vehicles (UAVs) are able to provide instantaneous visual cues and a high-level data throughput that could be further leveraged to address complex tasks, such as semantically rich scene understanding. In this work, we built on the use of Large Language Models (LLMs) and Visual Language Models (VLMs), together with a state-of-the-art detection pipeline, to provide thorough zero-shot UAV scene literary text descriptions. The generated texts achieve a GUNNING Fog median grade level in the range of 7–12. Applications of this framework could be found in the filming industry and could enhance user experience in theme parks or in the advertisement sector. We demonstrate a low-cost highly efficient state-of-the-art practical implementation of microdrones in a well-controlled and challenging setting, in addition to proposing the use of standardized readability metrics to assess LLM-enhanced descriptions.

Who reads Semantic Scene Understanding with Large Language Models on Unmanned Aerial Vehicles?

Author

J. de Curtò; I. de Zarzà; Carlos T. Calafate

Publisher

MDPI AG

Published

2023

Language

EN

Field

Engineering (Physical Sciences)