UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration

arXiv:2401.15647v2 Announce Type: replace
Abstract: Over the past decade, automated methods have been developed to detect cracks more efficiently, accurately, and objectively, with the ultimate goal of replacing conventional manual visual inspection techniques. Among these methods, semantic segmentation algorithms have demonstrated promising results in pixel-wise crack detection tasks. However, training such networks requires a large amount of human-annotated datasets with pixel-level annotations, which is a highly labor-intensive and time-consuming process. Moreover, supervised learning-based methods often struggle with poor generalizability in …

abstract adversarial algorithms arxiv automated cs.ai cs.cv detection eess.iv however image image restoration pixel restoration results segmentation semantic tasks training type unsupervised via visual visual inspection wise