  Deep learning plays an important role in crack segmentation, but most work
utilize off-the-shelf or improved models that have not been specifically
developed for this task. High-resolution convolution neural networks that are
sensitive to objects' location and detail help improve the performance of crack
segmentation, yet conflict with real-time detection. This paper describes
HrSegNet, a high-resolution network with semantic guidance specifically
designed for crack segmentation, which guarantees real-time inference speed
while preserving crack details. After evaluation on the composite dataset
CrackSeg9k and the scenario-specific datasets Asphalt3k and Concrete3k,
HrSegNet obtains state-of-the-art segmentation performance and efficiencies
that far exceed those of the compared models. This approach demonstrates that
there is a trade-off between high-resolution modeling and real-time detection,
which fosters the use of edge devices to analyze cracks in real-world
applications.
