改进YOLOv11实现复杂环境下高精度天际线检测
YUNet: Improved YOLOv11 Network for Skyline Detection
- 将YOLOv11改造为类UNet结构，增强多尺度特征融合能力
- 在Skyfinder数据集上分割IoU达0.9858，在CH1上检测误差仅1.36像素
- 适合遥感图像处理、智能导航与港口安防等场景使用
天际线检测在地理定位、飞行控制、视觉导航和港口安全等领域具有重要意义。由于天气和光照变化导致天空与非天空区域外观差异大，检测面临挑战。本文提出YUNet算法，改进YOLOv11架构以实现复杂多变环境下的天空区域分割与天际线提取。通过构建包含编码器、颈部和解码器的类UNet结构，提升多尺度与远距离上下文特征融合能力：编码器从图像中提取多尺度特征，颈部实现特征融合，解码器利用融合特征重建预测结果。在Skyfinder和CH1数据集上分别进行分割与天际线检测验证，结果显示YUNet分割的IoU达到0.9858，天际线检测平均误差仅为1.36像素。代码已开源：https://github.com/kuazhangxiaoai/SkylineDet-YOLOv11Seg.git。
原文摘要 · Abstract (English)
Skyline detection plays an important role in geolocalizaion, flight control, visual navigation, port security, etc. The appearance of the sky and non-sky areas are variable, because of different weather or illumination environment, which brings challenges to skyline detection. In this research, we proposed the YUNet algorithm, which improved the YOLOv11 architecture to segment the sky region and extract the skyline in complicated and variable circumstances. To improve the ability of multi-scale and large range contextual feature fusion, the YOLOv11 architecture is extended as an UNet-like architecture, consisting of an encoder, neck and decoder submodule. The encoder extracts the multi-scale features from the given images. The neck makes fusion of these multi-scale features. The decoder applies the fused features to complete the prediction rebuilding. To validate the proposed approach, the YUNet was tested on Skyfinder and CH1 datasets for segmentation and skyline detection respectively. Our test shows that the IoU of YUnet segmentation can reach 0.9858, and the average error of YUnet skyline detection is just 1.36 pixels. The implementation is published at https://github.com/kuazhangxiaoai/SkylineDet-YOLOv11Seg.git.
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