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-frequency enhancement convolution module (TriPathHFConv), receptive-field coordinate-attention convol...
| Authors | Hao Wang |
|---|---|
| Primary category | cs.CV |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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