  Structures suffer from the emergence of cracks, therefore, crack detection is
always an issue with much concern in structural health monitoring. Along with
the rapid progress of deep learning technology, image semantic segmentation, an
active research field, offers another solution, which is more effective and
intelligent, to crack detection Through numerous artificial neural networks
have been developed to address the preceding issue, corresponding explorations
are never stopped improving the quality of crack detection. This paper presents
a novel artificial neural network architecture named Full Attention U-net for
image semantic segmentation. The proposed architecture leverages the U-net as
the backbone and adopts the Full Attention Strategy, which is a synthesis of
the attention mechanism and the outputs from each encoding layer in skip
connection. Subject to the hardware in training, the experiments are composed
of verification and validation. In verification, 4 networks including U-net,
Attention U-net, Advanced Attention U-net, and Full Attention U-net are tested
through cell images for a competitive study. With respect to mean
intersection-over-unions and clarity of edge identification, the Full Attention
U-net performs best in verification, and is hence applied for crack semantic
segmentation in validation to demonstrate its effectiveness.
