LOGOS: Language-guided Oriented Object Detection in Aerial Scenes

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[Submitted on 9 Jul 2026]

Title:LOGOS: Language-guided Oriented Object Detection in Aerial Scenes

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Abstract:Object detection in geospatial scenes, such as satellite and aerial imagery, poses significant challenges due to the varying orientations and densities of objects, as well as the complex backgrounds inherent to remote sensing imagery. Traditional methods for oriented object detection have struggled to address issues such as angular discontinuity, fixed query sizes, and inefficiencies in handling sparse or cluttered scenes. In this paper, we propose LOGOS, a novel transformer-based approach that leverages textual prompts to guide the detection of oriented objects in aerial scenes. In particular, our proposed approach incorporates prompt-modulated content queries to dynamically adjust the model's focus based on the provided text, thereby improving object detection accuracy in complex environments. Empirically, extensive experiments on the DOTA dataset demonstrate that LOGOS outperforms existing state-of-the-art methods, particularly in densely packed and rotated object scenarios. Our approach offers a significant step forward in improving the robustness and scalability of oriented object detection in remote sensing applications.

Comments: Accepted to SOICT 2025

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.08004 [cs.CV]

(or arXiv:2607.08004v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.08004

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Trong-Thuan Nguyen [view email] [v1] Thu, 9 Jul 2026 00:11:48 UTC (10,244 KB)

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