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.