  Fracture is one of the main failure modes of engineering structures such as
buildings and roads. Effective detection of surface cracks is significant for
damage evaluation and structure maintenance. In recent years, the emergence and
development of deep learning techniques have shown great potential to
facilitate surface crack detection. Currently, most reported tasks were
performed by a convolutional neural network (CNN), while the limitation of CNN
may be improved by the transformer architecture introduced recently. In this
study, we investigated nine promising models to evaluate their performance in
pavement surface crack detection by model accuracy, computational complexity,
and model stability. We created 711 images of 224 by 224 pixels with crack
labels, selected an optimal loss function, compared the evaluation metrics of
the validation dataset and test dataset, analyzed the data details, and checked
the segmentation outcomes of each model. We find that transformer-based models
generally are easier to converge during the training process and have higher
accuracy, but usually exhibit more memory consumption and low processing
efficiency. Among nine models, SwinUNet outperforms the other two transformers
and shows the highest accuracy among nine models. The results should shed light
on surface crack detection by various deep-learning models and provide a
guideline for future applications in this field.
