摘要：Pipeline defect detection technology plays an important role in pipeline maintenance and transportation. Defect detection based on machine learning methods has gained considerable attention in practical engineering. However, it is still challenging to provide an accurate diagnosis and defect size estimation due to the poor inter-class discriminability and intra-class concentration. Such as, it is difficult to distinguish the hole defect, which is similar in appearance to dent defect. For this purpose, a multi-modal cascade detection framework of pipeline defects based on Deep Transfer Metric Learning (DTML) is proposed for defect recognition and defect size estimation, which integrates with machine vision and Magnetic Flux Leakage (MFL). DTML model based on ResNet50 is designed to extract discriminative features from defect images obtained through vision sensor. To enhance the features of MFL signals, Gramian Angular Field (GAF) is used to achieve the two-dimensional feature extraction. After that, three ResNet101 models are developed to estimate the pipeline defect size of different types. The experimental results demonstrate that the proposed multi-modal cascade detection framework performs well in defect recognition and defect size estimation. 漏 2024 Elsevier Ltd

ISSN号：1350-6307

卷、期、页：卷160

发表日期：2024-06-01

影响因子：0.000000

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 Failure Analysis

通讯作者：高博轩,苗兴园

第一作者：赵弘

论文类型：期刊论文

论文概要：高博轩,赵弘,苗兴园，Multi-modal cascade detection of pipeline defects based on deep transfer metric learning，Engineering Failure Analysis，2024，卷160

论文题目：Multi-modal cascade detection of pipeline defects based on deep transfer metric learning