| contributor author | Zhang, Huan | |
| contributor author | Liu, Pengfei | |
| contributor author | Zhang, Xiaoguo | |
| contributor author | Yang, Yuan | |
| contributor author | Liu, Youchen | |
| contributor author | Wang, Qing | |
| date accessioned | 2026-08-20T11:12:19Z | |
| date available | 2026-08-20T11:12:19Z | |
| date copyright | 2026/06/03 | |
| date issued | 2026 | |
| identifier other | JITSE4.ISENG-2864.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311841 | |
| description abstract | AbstractPavement crack detection is crucial for maintaining urban infrastructure and ensuring
traffic safety. However, existing methods often struggle with issues such as poor
accuracy in complex environments, inadequate handling of diverse crack ... | |
| publisher | American Society of Civil Engineers | |
| title | SD-YOLO: An Efficient Deep Learning Framework for Real-Time and Multiscale Pavement Crack Detection | |
| type | Journal Article | |
| journal volume | 32 | |
| journal issue | 3 | |
| journal title | Journal of Infrastructure Systems | |
| identifier doi | 10.1061/JITSE4.ISENG-2864 | |
| journal fristpage | 04026013-1 | |
| journal lastpage | 04026013-16 | |
| page | 16 | |
| tree | Journal of Infrastructure Systems:;2026:;Volume ( 032 ):;issue: 003 | |
| contenttype | Fulltext | |
