The application of an increasing number of new
technologies in tunnel construction is a consequence of the rapid development
of intelligent tunnel construction. Among these, the use of machine vision
instead of human eyes for geological information recognition in tunnels has
become increasingly widespread. The authors build on this trend by using
machine vision to investigate intelligent classification methods for the
lithology of the tunnel face. First, four types of lithology (limestone,
mudstone, sandstone, and basalt) are screened and counted by collecting highdefinition
tunnel face photos, geological sketches, and engineering geological
descriptions along the highspeed railway. On this
basis, image enhancement techniques are used to expand the number of samples
and construct a lithology sample set. Then, based on the above sample sets, the
ResNet50V2 lithology classification transfer learning model and the VIT
lithology classification model are constructed. The lithology classification
effects of the two models are compared, and the Stacking method is used to
integrate the classification characteristics of the two models. The authors
select the most suitable metalearner by comparing the
integrated fusion effects of three metalearners,
logistic regression(LR), support vector machine, and decision tree, on the two
models. The results reveal that the Stacking model constructed by LR integrated
with ResNet50V2 and VIT has the best classification effect on lithology. Apart
from having an accuracy rate of 93.8%, it can also fully integrate the overall
and local features of the lithology of the tunnel face for better
classification.
XIANG Lulu
,
TONG Jianjun
,
WANG Mingnian
,
MIAO Xingwang
,
YE Pei
. Ensemble Convolutional Neural Networks and Visual
Transformers: Research on Tunnel Face Rock Identification[J]. Tunnel Construction, 2024
, 44(5)
: 1056
-1067
.
DOI: 10.3973/j.issn.2096-4498.2024.05.013
