Quantitative Assessment of Pipeline Defects Utilizing a Dual-Stage Deep Learning Framework: Integration of Pretrained Yolov8 and Cnn Architectures on Magnetic Flux Leakage Data DOI

Jialiang Xie,

Jie Yang,

Kuan Fu

et al.

Published: Jan. 1, 2024

As long-distance oil pipelines near the end of their operational tenure, propensity for leakage due to localized defects markedly increases, necessitating imperative systematic inspection and sustained maintenance efforts. Magnetic Flux Leakage (MFL) inspection, a mainstream non-destructive testing methodology, has been extensively adopted. In light voluminous nature monitoring data, deep learning computer vision technologies play pivotal role in enhancing efficiency accuracy detection. This study introduces an innovative cascading detection technique that amalgamates advanced visual recognition network YOLOv8 with novel multi-input parallel convolution structure. Through channel fusion-based image preprocessing techniques, it adeptly utilizes tri-axial MFL experimental data precisely localize pipeline defects, while concurrently predicting sizes depths defects. research meticulously investigates impact various processing techniques model architectures on defect quantifiable prediction. Following stringent validation, our method demonstrated superiority over conventional approaches quantitative assessment tasks. Moreover, proposed significantly outperforms single-input prediction networks predictive concerning highlighting its prospective utility gas through improved precision, timeliness, economic interventions.

Language: Английский

Translation of MFL and UT data by using generative adversarial networks: A comparative study DOI Creative Commons
Jiatong Ling, Xiang Peng,

Matthias Peussner

et al.

NDT & E International, Journal Year: 2024, Volume and Issue: unknown, P. 103246 - 103246

Published: Oct. 1, 2024

Language: Английский

Citations

1
Quantitative Assessment of Pipeline Defects Utilizing a Dual-Stage Deep Learning Framework: Integration of Pretrained Yolov8 and Cnn Architectures on Magnetic Flux Leakage Data DOI

Jialiang Xie,

Jie Yang,

Kuan Fu

et al.

Published: Jan. 1, 2024

As long-distance oil pipelines near the end of their operational tenure, propensity for leakage due to localized defects markedly increases, necessitating imperative systematic inspection and sustained maintenance efforts. Magnetic Flux Leakage (MFL) inspection, a mainstream non-destructive testing methodology, has been extensively adopted. In light voluminous nature monitoring data, deep learning computer vision technologies play pivotal role in enhancing efficiency accuracy detection. This study introduces an innovative cascading detection technique that amalgamates advanced visual recognition network YOLOv8 with novel multi-input parallel convolution structure. Through channel fusion-based image preprocessing techniques, it adeptly utilizes tri-axial MFL experimental data precisely localize pipeline defects, while concurrently predicting sizes depths defects. research meticulously investigates impact various processing techniques model architectures on defect quantifiable prediction. Following stringent validation, our method demonstrated superiority over conventional approaches quantitative assessment tasks. Moreover, proposed significantly outperforms single-input prediction networks predictive concerning highlighting its prospective utility gas through improved precision, timeliness, economic interventions.

Language: Английский

Citations

0