基于三分量自主意识UAV路面遥感图像拼接算法

针对在检测过程中需要高分辨率的全景图像分析路面病害特征参数的问题，提出了一种具有三分量（3c）动态自主意识的遥感路面病害图像拼接算法。基于目标识别、主动立体视觉确定UAV系统在三维结构中的自主意识；采用增量搜索策略基于几何约束将SIFT点特征三角化；结合PNP—RANSAC算法找到最佳匹配面进行误匹配剔除；利用三角函数理论对图像进行融合。实现对长线程病害路面图像数据集的快速准确拼接。特征搜索时间平均为未使用增量搜索策略的43.45%，拼接准确率在88%以上。实验数据表明，基于三分量自主意识的UAV路面遥感图像拼接系统可以有效地应对长线程路面病害图像。

Stitching algorithm of UAV pavement remote sensing image with three-component autonomy consciousness

This paper presented a remote sensing image mosaic algorithm for pavement diseases. There is a dynamic sensed of autonomy in its three components(3c). The algorithm solved a problem in the detection process, which needed to parameterize the pavement disease information with high-resolution panoramic images. This paper determined the autonomous consciousness of the UAV system in a three-dimensional structure based on target detection and stereo active vision; used an incremental search strategy to triangulate SIFT point features based on geometric constraints; combined the PNP-RANSAC algorithm to find the best matching surface for eliminating the wrong matching and used trigonometric function theory to fuse the images. It realized the fast and accurate splicing of long-thread data sets of diseased pavement. The average feature search time is 43.45% of that without the incremental search strategy, and the stitching accuracy is over 88%. The experimental data show that the three-component autonomous awareness-based UAV pavement remote sensing image stitching system can effectively cope with long-lane pavement distress images.