Jan. 31, 2024, 3:43 p.m. | Zehao Ye Lucy Lovell Asaad Faramarzi Jelena Ninic

cs.CV updates on arXiv.org arxiv.org

Automating visual inspection for capturing defects based on civil structures appearance is crucial due to its currently labour-intensive and time-consuming nature. An important aspect of automated inspection is image acquisition, which is rapid and cost-effective considering the pervasive developments in both software and hardware computing in recent years. Previous studies largely focused on concrete and asphalt, with less attention to masonry cracks. The latter also lacks publicly available datasets. In this paper, we first present a corresponding data set for …

acquisition automated automation civil computing cost cs.cv defects detection hardware image instance labour nature sam segmentation software visual visual inspection

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Lead Data Modeler

@ Sherwin-Williams | Cleveland, OH, United States