Intelligently identifying
cracks in metro tunnels is challenging due to factors such as short skylight
detection time, low manual detection efficiency, inconspicuous crack
characteristics, and various disturbances in the tunnel. Therefore, a modified
YOLO (M-YOLO) algorithm based on
YOLOv8 is proposed to efficiently and intelligently identify cracks in tunnels.
This algorithm adopts full-dimensional dynamic
convolution to replace the traditional convolution module, remarkably improving
the detection accuracy and avoiding model parameter expansion. The C2fGC module
is introduced to improve the network structure, establishing a new feature
extraction and dimensionality reduction mechanism and enhancing the high-level feature representation. The convolutional block attention
module attention mechanism module is introduced to strengthen feature learning
and extraction of crack region images and reduce background interference,
effectively improving the detection accuracy. The WIOU loss function is
introduced to adjust the degree of penalty of geometric factors, which improves
the generalization ability of the model and helps it perform better even with
low-quality data samples. The test results show that in
the real samples, which are used for identifying cracks in a metro tunnel, the
mean accuracy of the M-YOLO algorithm is as high as
83.0%, which indicates a significant improvement of 15.7% compared with the
original model.
BAO Yan
,
MEI Chongbin
,
XU Pengyu
,
SUN Zhe
,
WEN Yucheng
. Crack Identification in
Metro Tunnels Based on Improved YOLOv8 Algorithm[J]. Tunnel Construction, 2024
, 44(10)
: 1961
-1970
.
DOI: 10.3973/j.issn.2096-4498.2024.10.005
