Show simple item record

contributor authorZhang, Huan
contributor authorFeng, Jinyan
contributor authorDai, Chenghao
contributor authorTong, Zhaoxia
date accessioned2026-08-20T21:24:54Z
date available2026-08-20T21:24:54Z
date copyright2025/08/20
date issued2025
identifier otherJCCEE5.CPENG-6675.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314414
description abstractAbstractThis study proposes a novel pavement crack segmentation methodology that integrates synthetic data sets with unsupervised domain adaptation to address annotation challenges in pavement crack data sets and enhance segmentation performance. An ...Practical ApplicationsAutomated crack detection plays a crucial role in structural health monitoring by enhancing speed, precision, and reliability. However, training deep learning models necessitates a vast amount of labeled real-world data, posing ...
publisherAmerican Society of Civil Engineers
titlePavement Crack Segmentation Based on Synthetic Data Sets and Unsupervised Domain Adaptation
typeJournal Article
journal volume39
journal issue6
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-6675
journal fristpage04025100-1
journal lastpage04025100-17
page17
treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 006
contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record