A skyline detection algorithm based on improved YOLO11

doi:10.13474/j.cnki.11-2246.2026.0503

Abstract

Abstract: [Purposes] Skyline detection plays an important role in geolocalization,flight control,visual navigation,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.[Methods]For these challenges,we proposes the YUNet algorithm,which improves the YOLO11 architecture to segment the sky region and extract the skyline in complicated and variable circumstances.In this research,the YOLO11 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 is tested on Skyfinder,CH1 datasets for segmentation and skyline detection,respectively.[Findings] The test shows that the IoU of YUNet segmentation can reach 0.986,and the average error of YUNet skyline detection is just 1.36 pixels.[Conclusions] YUNet has an excellent performance and speed.And it can complete the sky segment and skyline detection task in the complex environment,which is valuable for engineering applications.

Key words: skyline detection, YOLO11, deep learning, image processing

CLC Number:

P237

YANG Gang, WANG Miao, CHEN Si, ZHOU Quan, LI Jiangchuan. A skyline detection algorithm based on improved YOLO11[J]. Bulletin of Surveying and Mapping, 2026, 0(5): 12-16.

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