基于深度学习LoFTR算法的路面图像拼接

相比基于特征点的传统图像特征匹配算法,基于深度学习的特征匹配算法能产生更大规模和更高质量的匹配.为获取较大范围且清晰的路面裂缝图像,并解决弱纹理图像拼接过程中发生的匹配对缺失问题,本文基于深度学习LoFTR (detector-free local feature matching with Transformers)算法实现路面图像的拼接,并结合路面图像的特点,提出局部拼接方法缩短算法运行的时间.先对相邻图像做分割处理,再通过LoFTR算法产生密集特征匹配,根据匹配结果计算出单应矩阵值并实现像素转换,然后通过基于小波变换的图像融合算法获得局部拼接后的图像,最后添加未输入匹配网络的部分图像,得到相邻图像的完整拼接结果.实验结果表明,与基于SIFT (scale-invariant feature transform)、SURF (speeded up robust features)、ORB (oriented FAST and rotated BRIEF)的图像拼接方法比较,研究所提出的拼接方法对路面图像的拼接效果更佳,特征匹配阶段产生的匹配结果置信度更高.对于两幅路面图像的拼接,采...

Road Image Mosaic Based on Deep Learning LoFTR Algorithm

The feature matching algorithm based on deep learning can produce larger scale and higher quality matching than the traditional algorithm based on feature points. This study aims to obtain a wide range of clear pavement crack images and solve the problem of missing matching pairs in weak texture image mosaics. The road image mosaic is realized based on the deep learning LoFTR (detector-free local feature matching with Transformers) algorithm. Given the characteristics of road images, the local mosaic method is proposed to shorten the running time of the algorithm. Firstly, the segmentation of adjacent images is conducted, and the dense feature matching is produced through the LoFTR algorithm. Secondly, the homography matrix value is calculated according to the matching results and the pixel conversion is realized. Thirdly, images after local mosaics are obtained through the image fusion algorithm based on wavelet transform. Finally, some images that are not input into the matching network are added to get the complete mosaic result of adjacent images. The experimental results show that, compared with methods based on SIFT (scale-invariant feature transform), SURF (speeded up robust features), and ORB (oriented FAST and army), the proposed method has a better effect on road image mosaic and higher confidence of matching results in feature matching stage. For the mosaic of two road images, the time consumed by the local splicing method is shortened by 27.53% compared with that before the improvement. The proposed mosaic scheme is efficient and accurate, which can provide overall disease information for road disease monitoring.