Challenges posed by
hydraulic tunnels, such as confined space, varying cross-sections, complex environments, and lack of signal, hinder rapid
image acquisition. To address these challenges, a rapid image acquisition
system that can capture cross-sectional images of the
tunnel while traveling in a short period is presented. This system can capture
images under different cross-sectional shapes and sizes
and meets the acquisition overlap rate and accuracy requirements of three-dimensional reconstruction and image generator for lining expansion.
To enhance the speed and accuracy of the traditional algorithms, the YOLOv5
detection algorithm is improved using a DenseNet network and an efficient
channel attention module, thereby enhancing the effectiveness of feature
extraction. In addition, a "segment-ring-point" method is proposed for the efficient identification of
common surface defects on tunnel linings using panoramic images, with the
bottom image of the panorama serving as the seed point. This approach addresses
the limitations of single images that fail to reflect the overall distribution
of the damage and large images that do not capture the effective features of
the network. Field applications demonstrate the following: (1) The improved
YOLOv5 model significantly improves the mean average precision and F1 performances compared with YOLOv7-tiny, YOLOv8, and YOLOv9 models, effectively improving the accuracy
of defect identification and boundary positioning in complex environments and
substantially reducing the occurrence of false positives and missed detections.
(2) This approach is suitable for rapid acquisition and panoramic image
stitching in hydraulic tunnels with diverse cross-sectional
shapes and sizes. It can also be used for identifying and locating surface
diseases promptly in tunnels to provide defect treatment measures.
FU Jinyang
,
WANG Haoyu
,
XIE Jichen
,
ZHU Zhiheng
,
ZHANG Cong
,
WU Jun
. Rapid Detection Methods for
Seepage and Erosion Diseases in Hydraulic Tunnels Using Panoramic Image[J]. Tunnel Construction, 2024
, 44(10)
: 1971
-1981
.
DOI: 10.3973/j.issn.2096-4498.2024.10.006
