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 scaleinvariant
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 vehiclemounted
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 GPUcentral 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 SIFTGPU can improve imagematching 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.
ZHANG Dongliang
,
GONG Zhiqun
,
YANG Shiting
,
LU Yening
,
ZHAN Jing
,
AN Xiaoya
. Tunnel Image Stitching Based on ScaleInvariant
Feature TransformGraphics Processing
Unit and Integral Adjustment Method[J]. Tunnel Construction, 2023
, 43(7)
: 1118
-1126
.
DOI: 10.3973/j.issn.2096-4498.2023.07.004
