LOGOS: Language-guided Oriented Object Detection in Aerial Scenes

2026-07-08

AI Summary

This work addresses the challenges of oriented object detection in remote sensing imagery, where targets exhibit arbitrary orientations, uneven densities, and complex backgrounds. To tackle these issues, the authors propose a novel language-guided oriented object detection method based on Transformer architecture. This approach introduces textual prompts into remote sensing oriented detection for the first time, dynamically modulating content queries to achieve deep fusion between linguistic semantics and visual features. This design effectively mitigates angle discontinuity problems and overcomes the limitations of fixed queries in both sparse and dense scenarios. Experimental results on the DOTA dataset demonstrate that the proposed method significantly outperforms existing state-of-the-art approaches, particularly excelling in high-density and highly rotated scenes, thereby enhancing model robustness and scalability.

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.

Problem

• oriented object detection
• aerial imagery
• remote sensing
• angular discontinuity
• object density

Innovation

• language-guided detection
• oriented object detection
• prompt-modulated queries
• aerial imagery
• transformer-based remote sensing