AI 中文总结
针对航拍小目标检测中高频信息丢失和全局上下文建模难的问题，提出基于YOLO12的检测器，结合高频增强卷积、坐标注意力和Mamba模块，在VisDrone上达到60.0% mAP@50。
AI 中文摘要
无人机（UAV）技术的快速发展使得航拍图像目标检测在自然资源监测、交通管理和灾害响应中日益重要。在航拍图像中检测小目标仍然困难，因为目标仅占据极少数像素，高频线索容易丢失，并且在杂乱场景中难以建模全局上下文。现有检测器通常保留的边缘、角落和纹理信息不足。我们提出了\ours，一种基于YOLO12架构构建的航拍图像检测器。该模型结合了三路径高频增强卷积模块（TriPathHFConv）、感受野坐标注意力卷积（RFCAConv）以及基于Mamba的全局上下文模块。在VisDrone数据集上，输入分辨率为960*960时，我们的方法达到了60.0%的mAP@50和38.6%的mAP@50:95，展示了在小目标检测中良好的精度-效率权衡。基准测试和数据集协议遵循VisDrone挑战赛的设置。
英文摘要
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 convolution (RFCAConv), and a Mamba-based global-context module. On VisDrone, at an input resolution of 960*960, ours achieves 60.0% mAP@50 and 38.6%mAP@50:95, demonstrating a favorable accuracy--efficiency trade-off for small-object detection. The benchmark and dataset protocol follow the VisDrone challenge setup.
