SOURCE-LINKED INTELLIGENCE
YOLO12-MambaScan: An Efficient Object Detector with High-Frequency Enhancement and State-Space Modeling
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 aerial images remains difficult because objects occupy very few pixels, high-frequency cues are easily lost, and global context is hard to model in cluttered scenes. Existing detectors often retain insufficient edge, corner, and texture information. We propose \ours, an aerial-image detector built on the YOLO12 architecture. The model combines a triple-path high-freq
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T01:58:35.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.