YUNet: Improved YOLOv11 Network for Skyline Detection

TL;DR

YUNet enhances YOLOv11 by integrating an UNet-like architecture for more accurate skyline detection and segmentation in variable weather and illumination conditions, demonstrating high IoU and low detection error.

Contribution

The paper introduces YUNet, a novel architecture that extends YOLOv11 with multi-scale feature fusion for improved skyline detection in complex environments.

Findings

IoU of 0.9858 in segmentation

Average skyline detection error of 1.36 pixels

Effective in variable weather and illumination conditions

Abstract

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.