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Intelligent Identification for Imbalanced Oil–Gas Pipeline Defects Based on Triple Attention Mechanism and Dual-Modal Deep Convolution Network

dc.contributor.authorLiao, Chunyan
dc.contributor.authorLiang, Wei
dc.contributor.authorLiu, Shuanglei
dc.contributor.authorHuang, Tianchang
dc.contributor.authorWu, Linyu
dc.date.accessioned2026-08-20T12:06:40Z
dc.date.available2026-08-20T12:06:40Z
dc.date.copyright2026/03/30
dc.date.issued2026
dc.identifier.otherJPSEA2.PSENG-1971.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313110
dc.description.abstractAbstractThe class imbalance of crack defects in oil and gas pipelines presents a significant challenge to the diagnosis of pipeline integrity, and the scarcity of high-risk cracks can easily lead to the recognition models tending to predict them as the ...
dc.publisherAmerican Society of Civil Engineers
dc.titleIntelligent Identification for Imbalanced Oil–Gas Pipeline Defects Based on Triple Attention Mechanism and Dual-Modal Deep Convolution Network
dc.typeJournal Article
dc.journal.volume17
dc.journal.issue3
dc.journal.titleJournal of Pipeline Systems Engineering and Practice
dc.identifier.doi10.1061/JPSEA2.PSENG-1971
dc.journal.fristpage04026023-1
dc.journal.lastpage04026023-20
dc.page20
dc.treeJournal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 003
dc.contenttypeFulltext


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