| dc.contributor.author | Fan, Ching-Lung | |
| dc.date.accessioned | 2026-08-20T10:40:09Z | |
| dc.date.available | 2026-08-20T10:40:09Z | |
| dc.date.copyright | 2026/01/06 | |
| dc.date.issued | 2026 | |
| dc.identifier.other | JCEMD4.COENG-16994.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311097 | |
| dc.description.abstract | AbstractDeep learning–based computer vision technology has become an effective tool for crack
detection and segmentation, and it has considerable potential for application in infrastructure
maintenance. Crack detection and segmentation are the two core ...Practical ApplicationsIdentifying and measuring cracks in concrete structures is essential for ensuring
their safety and longevity. This study presents a practical approach to detecting
and analyzing concrete cracks using computer vision technology. By ... | |
| dc.publisher | American Society of Civil Engineers | |
| dc.title | Crack Identification and Severity Analysis via Computer Vision: Comparative Study of RGB and Grayscale Imagery | |
| dc.type | Journal Article | |
| dc.journal.volume | 152 | |
| dc.journal.issue | 3 | |
| dc.journal.title | Journal of Construction Engineering and Management | |
| dc.identifier.doi | 10.1061/JCEMD4.COENG-16994 | |
| dc.journal.fristpage | 04026004-1 | |
| dc.journal.lastpage | 04026004-17 | |
| dc.page | 17 | |
| dc.tree | Journal of Construction Engineering and Management:;2026:;Volume ( 152 ):;issue: 003 | |
| dc.contenttype | Fulltext | |
