论文标题
通过对抗图像到频率变换，无监督的像素级的道路缺陷检测
Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
论文作者
论文摘要
在过去的几年中，由于对计算机视觉和深度学习的各种研究的进步，道路缺陷检测的性能得到了极大的改善。尽管大规模且井井有条的数据集在某种程度上提高了检测道路路面缺陷的性能，但得出一个模型，该模型仍然可以在实践中可靠地执行的模型，因为在实践中，考虑到多样化的道路条件和缺陷模式，它很难构建数据集。为了结束这一点，我们提出了一种使用对抗图像到频率变换（AIFT）的无监督方法来检测道路缺陷。 AIFT在得出缺陷检测模型时采用了无监督的方式和对抗性学习，因此AIFT不需要道路路面缺陷的注释。我们使用GAPS384数据集，CrackTree200数据集，Crack500数据集和CFD数据集评估了AIFT的效率。实验结果表明，所提出的方法检测到各种道路检测，并且表现优于现有的最新方法。
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.
