Abstract: The existing tunnel image stitching is inefficient and prone to large cumulative errors. Therefore, a technology for tunnel image stitching based on a collaborative processing algorithm of scaleinvariant feature transform(SIFT)graphics processing unit(GPU) and integral adjustment method is required. First, the image and point cloud data inside the tunnel are obtained using vehiclemounted mobile measurement technology; the original image is corrected using point cloud data and collinear equation, and the light and color are homogenized using the MASK smoothing algorithm and Wallis filter smoothing method. Then, the SIFT matching algorithm is integrated using a collaborative processing technology of the GPUcentral processing unit to extract feature points of all images to be stitched, and the random sample consensus algorithm is used to remove mismatches. Finally, a translation rotation scaling model is used to stitch 400 images, the global adjustment algorithm is used to compensate for the error, and the block processing technology of the geospatial data abstraction library is used to output the results. According to the experimental findings, the collaborative processing algorithm of SIFTGPU can improve imagematching efficiency by 3 to 25 times compared to the original SIFT matching algorithm. Compared to the original algorithm, the integral adjustment algorithm can more effectively address the error accumulation problem caused by the increase of the image to be stitched, resulting in a threefold reduction in error.

Key words: tunnel, image stitching, scaleinvariant feature transform, random sample consensus, integral adjustment