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YOLO12-MambaScan enhances aerial object detection with state-space modeling

By PulseAugur Editorial · [1 sources] ·

Researchers have developed YOLO12-MambaScan, a new object detection model designed for aerial imagery. This model enhances the YOLO12 architecture by incorporating a novel high-frequency enhancement convolution module and a Mamba-based global-context module. These additions aim to improve the detection of small objects in cluttered scenes by preserving crucial edge, corner, and texture information. In tests on the VisDrone dataset, YOLO12-MambaScan achieved a 60.0% mAP@50, showcasing a strong balance between accuracy and efficiency for aerial object detection tasks. AI

IMPACT This model improves small object detection in aerial imagery, potentially aiding applications like resource monitoring and traffic management.

RANK_REASON This is a research paper detailing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

YOLO12-MambaScan enhances aerial object detection with state-space modeling

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This is a research paper detailing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hao Wang ·

    YOLO12-MambaScan: An Efficient Object Detector with High-Frequency Enhancement and State-Space Modeling

    arXiv:2609.13647v1 Announce Type: new Abstract: The rapid development of unmanned aerial vehicle (UAV) technology has made aerial-image object detection increasingly important for natural-resource monitoring, traffic management, and disaster response. Detecting small objects in a…

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