Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
This addresses the challenge of reliable road defect detection under diverse conditions for infrastructure maintenance, though it is incremental as it builds on unsupervised and adversarial learning techniques.
The paper tackles the problem of road defect detection without requiring annotated data by proposing an unsupervised approach using Adversarial Image-to-Frequency Transform (AIFT), which outperforms existing state-of-the-art methods on multiple datasets.
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.