The existing crack
detection algorithms for highway tunnels exhibit drawbacks such as inadequate
feature extraction and weak antiinterference
ability, leading to missed detections and slow detection speeds. Therefore, a
tunnel crack detection method based on improved YOLOv5 is proposed. First, the
residual module C3SM is introduced into the backbone network along with an
efficient threedimensional parameter-free attention mechanism to enhance the interaction between deep and
shallow feature information. This enhances the feature extraction capability of
the network while optimizing computational complexity. Second, a new feature
fusion network structure is employed in the feature pyramid to integrate the
feature maps of neighboring layers, better preserving crack edge information
and accelerating model detection without loss of semantic information. Finally,
a position loss function, WIoU, is used to optimize the detection of occluded
and overlapped targets. To validate the effectiveness of this method, extensive
experiments are conducted on the Tunnel-crack and
Huzhou tunnel crack datasets. The results show that the proposed method
achieves 88.4% and 103.5 frames/s and 85.1% and 99.4 frames/s in terms of
accuracy and speed, respectively. These results demonstrate a higher crack
detection accuracy than most highperformance target
detectors.
WU Bin
,
YU Shuangling
,
CHEN Yangyang
,
ZHAO Jie
. Tunnel Crack Detection
Method Based on Three-Dimensional Parameter-Free Attention
Mechanisms[J]. Tunnel Construction, 2024
, 44(7)
: 1520
-1531
.
DOI: 10.3973/j.issn.2096-4498.2024.07.018
