提出一种用于固定翼无人机自主着陆导航的分层机场检测方法,以提高机场检测的速度。采取一种由粗到细的分层搜索结构,逐层缩小搜索面积以快速提取机场的候选区域。首先进行伪地平线检测将机场搜索区域限制为地面区域,然后根据机场区域包含大量垂直线的事实确定机场近似区域候选区以进一步缩小机场搜索区域,最后利用Edge Boxes得到高定位精度的机场候选区域。利用局部约束线性编码(LLC)特征学习法以尺度不变特征变换(SIFT)为基础特征提取机场候选区域特征并使用线性支持向量机(SVM)分类器完成机场检测。实验中在不同天气、不同背景条件下对所提机场检测方法进行了综合测试,并与其他方法进行比较,实验结果表明本文机场检测方法能有效提高机场检测速度,且准确率高。

An airport detection method is proposed for the navigation of fixed-wing unmanned aerial vehicle (UAV) autonomous landing in this paper, which aims at improving the efficiency of detection. A hierarchical architecture is adopted to obtain airport candidate regions which reduces the search space gradually. The pseudo horizon is detected to limit the searching space to the ground area, then candidate approximate airport area is acquired based on the fact that the airport area contains lots of orthogonal line segments. Edge Boxes is adopted to obtain proposals with good localization on the candidate approximate airport areas. Locality-constrained linear coding (LLC) is used for feature extraction with scale-invariant feature transformation (SIFT) as the basic features and linear support vector machine (SVM) is used to finish the task of airport detection. We evaluate the proposed method under different conditions and compare it with other methods. The results show that our method improves the efficiency of airport detection and has a higher average precision.