| contributor author | Han, Beilin | |
| contributor author | Zhang, Yihang | |
| contributor author | Huang, Chuyue | |
| contributor author | Ding, Wei | |
| contributor author | Liu, Zhiwei | |
| contributor author | Deng, Hongyang | |
| contributor author | Wu, Jie | |
| date accessioned | 2026-08-20T21:26:32Z | |
| date available | 2026-08-20T21:26:32Z | |
| date copyright | 2025/11/21 | |
| date issued | 2026 | |
| identifier other | JCCEE5.CPENG-6951.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314458 | |
| description abstract | AbstractEfficient crack detection is vital for infrastructure safety, yet many deep learning
models sacrifice practicality for precision, demanding resources beyond the reach
of field-deployable devices. This paper presents UMDA (U-MobileNetV3-DECA-AUX), ... | |
| publisher | American Society of Civil Engineers | |
| title | UMDA: Lightweight and Efficient Crack Segmentation Model | |
| type | Journal Article | |
| journal volume | 40 | |
| journal issue | 2 | |
| journal title | Journal of Computing in Civil Engineering | |
| identifier doi | 10.1061/JCCEE5.CPENG-6951 | |
| journal fristpage | 04025143-1 | |
| journal lastpage | 04025143-18 | |
| page | 18 | |
| tree | Journal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 002 | |
| contenttype | Fulltext | |
