  In the past few years, the performance of road defect detection has been
remarkably improved thanks to advancements on various studies on computer
vision and deep learning. Although a large-scale and well-annotated datasets
enhance the performance of detecting road pavement defects to some extent, it
is still challengeable to derive a model which can perform reliably for various
road conditions in practice, because it is intractable to construct a dataset
considering diverse road conditions and defect patterns. To end this, we
propose an unsupervised approach to detecting road defects, using Adversarial
Image-to-Frequency Transform (AIFT). AIFT adopts the unsupervised manner and
adversarial learning in deriving the defect detection model, so AIFT does not
need annotations for road pavement defects. We evaluate the efficiency of AIFT
using GAPs384 dataset, Cracktree200 dataset, CRACK500 dataset, and CFD dataset.
The experimental results demonstrate that the proposed approach detects various
road detects, and it outperforms existing state-of-the-art approaches.
