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arXiv · 2410.12979

BlabberSeg: Real-Time Embedded Open-Vocabulary Aerial Segmentation

Haechan Mark Bong·Ricardo de Azambuja·Giovanni Beltrame
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Abstract

Real-time aerial image segmentation plays an important role in the environmental perception of Uncrewed Aerial Vehicles (UAVs). We introduce BlabberSeg, an optimized Vision-Language Model built on CLIPSeg for on-board, real-time processing of aerial images by UAVs. BlabberSeg improves the efficiency of CLIPSeg by reusing prompt and model features, reducing computational overhead while achieving real-time open-vocabulary aerial segmentation. We validated BlabberSeg in a safe landing scenario using the Dynamic Open-Vocabulary Enhanced SafE-Landing with Intelligence (DOVESEI) framework, which uses visual servoing and open-vocabulary segmentation. BlabberSeg reduces computational costs significantly, with a speed increase of 927.41% (16.78 Hz) on a NVIDIA Jetson Orin AGX (64GB) compared with the original CLIPSeg (1.81Hz), achieving real-time aerial segmentation with negligible loss in accuracy (2.1% as the ratio of the correctly segmented area with respect to CLIPSeg). BlabberSeg's source code is open and available online.

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Haechan Mark Bong, Ricardo de Azambuja, Giovanni Beltrame. 2024-10-16. BlabberSeg: Real-Time Embedded Open-Vocabulary Aerial Segmentation. https://arxiv.org/abs/2410.12979

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