Estimation of Defect Size and Cross-Sectional Profile for the Oil and Gas Pipeline Using Visual Deep Transfer Learning Neural Network-Reference-Cited by-同舟云学术

Estimation of Defect Size and Cross-Sectional Profile for the Oil and Gas Pipeline Using Visual Deep Transfer Learning Neural Network

Author:

Zhang Min¹^ORCID,Guo Yanbao¹^ORCID,Xie Qiuju¹^ORCID,Zhang Yuansheng²^ORCID,Wang Deguo¹^ORCID,Chen Jinzhong³^ORCID

Affiliation:

1. College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, China

2. State Key Laboratory of Process Automation in Mining and Metallurgy, Beijing, China

3. China Special Equipment Inspection and Research Institute, Beijing, China

Funder

National Key Research and Development Program of China

State Key Laboratory of Process Automation in Mining and Metallurgy, and Beijing Key Laboratory of Process Automation in Mining and Metallurgy

Tribology Science Fund of State Key Laboratory of Tribology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

Reference40 articles.

5. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks;wang;Proc AAAI Workshops,2015

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