摘要：Girth weld defects (crack, lack of penetration, lack of fusion, and edge nibbling) can cause pipeline cracking failure accidents. Internal magnetic flux leakage (MFL) detection can successfully identify pipeline defects, while the intelligent identification of MFL signals based on deep learning can promote the accurate determination of pipeline girth weld defects. Although the YOLOv5 model can effectively identify abnormal image objects, it exhibits no attention preference during the feature extraction process, proving insufficient for small objects. This study targeted minor defects in the girth weld of the pipeline and used the Convolutional Block Attention Module (CBAM) to optimize the YOLOv5 network model structure, increasing detection network attention preference toward extracting small-target defect signals. The CBAM+YOLOv5 model improved the detection accuracy of the MFL signal of the girth weld in the pipeline from 89.33% to 98.11% and correctly identified and classified the MFL signal of the pipeline girth weld with 85% confidence, with minor anomalies. The CBAM+YOLOv5 model effectively improved the identification accuracy of the girth weld defect signal in the pipeline, providing tech-nical support for safety grade assessment and excavation verification.

关键字：YOLOv5; CBAM; Magnetic flux leakage; Internal detection; Image detection; Girth weld signal

ISSN号：0952-1976

卷、期、页：卷: 127子辑: B

发表日期：2024-01-01

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

参与作者：彭东华

通讯作者：徐鲁帅,魏昊天,钱伟超,任庆滢,王路明,马云栋

第一作者：董绍华

论文类型：期刊论文

论文概要：徐鲁帅,董绍华,魏昊天,彭东华,钱伟超,任庆滢,王路明,马云栋，Intelligent identification of girth welds defects in pipelines using neural networks with attention modules，ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE，2024，卷: 127子辑: B

论文题目：Intelligent identification of girth welds defects in pipelines using neural networks with attention modules