弱纹理飞机蒙皮曲面图像特征匹配及拼接

Weak texture aircraft skin curved image feature matching and stitching

为了解决弱纹理飞机蒙皮特征点分布不均匀、正确匹配的特征点对较少的问题,提出了一种改进的LoFTR算法对飞机蒙皮图像进行拼接。根据相机位姿利用柱面反投影对蒙皮图像进行曲面校正;通过图像之间的重叠区域确定特征提取区域,从而减少错误匹配点对的生成;使用LoFTR算法进行特征提取,并且使用RANSAC算法对特征点进行筛选;根据图像分块的思想对重叠区域进行网格划分来对特征点进一步筛选,使得特征点分布更加均匀,得到更加准确的变换矩阵进行图像配准。实验在自研无人车采集的飞机蒙皮图像上进行了测试和验证,改进的方法与SIFT、SURF、ORB、BRISK以及AKAZE进行了特征匹配率比较实验,SIFT、SURF、ORB、BRISK和AKAZE匹配率分别为4.84%,0.47%、2.9%、0.86%、5.08%,提出的算法特征匹配率达到55.21%,SSIM平均值提高了44.38%~88.46%。该方法适用于对飞机蒙皮图像的拼接任务,且不存在因弱纹理而导致漏拼的问题。

To address the issue of uneven distribution and insufficient correctly matched feature points in weakly-textured aircraft skin,an improved LoFTR algorithm is proposed for stitching aircraft skin images.Based on the posture of the camera,cylindrical back-projection is utilized to correct skin image curvature.By determining the feature extraction area through the overlapping regions between images,the generation of falsely matched point pairs is reduced.The LoFTR algorithm is employed for feature extraction,and the RANSAC algorithm is applied for feature point sorting.Adhering to the idea of image partitioning,grid division is used on overlapping areas for further sorting of feature points,ensuring a more even distribution,thereby yielding a more accurate transformation matrix for image registration.Experiments conducted on aircraft skin images collected via our self-developed unmanned vehicles confirmed the efficacy of this improved method.A feature matching rate comparison experiment with SIFT,SURF,ORB,BRISK,and AKAZE showed match rates of 4.84%,0.47%,2.9%,0.86%,and 5.08%,respectively,while the proposed algorithm achieved a feature match rate of 55.21%.The average SSIM increased by 44.38%to 88.46%.The proposed method is effective for stitching tasks of aircraft skin images,and it eliminates the issue of missed stitches due to weak textures.