Weld defect intelligent identification for oil and gas pipelines based on the deep learning models

被引:0
|
作者
Luo, Renze [1 ,2 ,3 ]
Wang, Lei [1 ]
机构
[1] School of Computer Science and Software Engineering, Southwest Petroleum University, Sichuan, Chengdu,610500, China
[2] State Key laboratory of Oil & Gas Reservoir Geology and Exploitation/, Southwest Petroleum University, Sichuan, Chengdu,610500, China
[3] School of Electrical Engineering and Information, Southwest Petroleum University, Sichuan, Chengdu,610500, China
关键词
D O I
10.3787/j.issn.1000-0976.2024.09.018
中图分类号
学科分类号
摘要
引用
收藏
页码:199 / 208
相关论文
共 50 条
  • [21] An intelligent garbage classifier based on deep learning models
    Liu, Jianghai
    MIPPR 2019: REMOTE SENSING IMAGE PROCESSING, GEOGRAPHIC INFORMATION SYSTEMS, AND OTHER APPLICATIONS, 2020, 11432
  • [22] A Deep Learning Based Automated Structural Defect Detection System for Sewer Pipelines
    Kumar, Srinath S.
    Abraham, Dulcy M.
    COMPUTING IN CIVIL ENGINEERING 2019: SMART CITIES, SUSTAINABILITY, AND RESILIENCE, 2019, : 226 - 233
  • [23] An adaptive defect detection method for underground cables pipelines based on deep learning
    Bai, Jingjing
    Han, Xinyu
    Cheng, Yunpen
    Feng, Xingming
    Qian, Chengwei
    2024 3RD INTERNATIONAL CONFERENCE ON ENERGY AND POWER ENGINEERING, CONTROL ENGINEERING, EPECE 2024, 2024, : 197 - 200
  • [24] Deep Learning-Based Automatic Defect Detection Method for Sewer Pipelines
    Shen, Dongming
    Liu, Xiang
    Shang, Yanfeng
    Tang, Xian
    SUSTAINABILITY, 2023, 15 (12)
  • [25] Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines: A Review
    Huang, Songling
    Peng, Lisha
    Sun, Hongyu
    Li, Shisong
    ENERGIES, 2023, 16 (03)
  • [26] Decision Tree-Based Approach for Defect Detection and Classification in Oil and Gas Pipelines
    Mohamed, Abduljalil
    Hamdi, Mohamed Salah
    Tahar, Sofiene
    PROCEEDINGS OF THE FUTURE TECHNOLOGIES CONFERENCE (FTC) 2018, VOL 1, 2019, 880 : 490 - 504
  • [27] IDENTIFICATION METHOD OF OIL PIPELINES TECHNICAL CONDITION BASED UPON MULTIGRAPH MODELS
    Vladova, A.
    PROCEEDINGS OF THE 10TH INTERNATIONAL PIPELINE CONFERENCE - 2014, VOL 4, 2014,
  • [28] Peanut Defect Identification Based on Multispectral Image and Deep Learning
    Wang, Yang
    Ding, Zhao
    Song, Jiayong
    Ge, Zhizhu
    Deng, Ziqing
    Liu, Zijie
    Wang, Jihong
    Bian, Lifeng
    Yang, Chen
    AGRONOMY-BASEL, 2023, 13 (04):
  • [29] A Review of Failure Prediction Models for Oil and Gas Pipelines
    Zakikhani, Kimiya
    Nasiri, Fuzhan
    Zayed, Tarek
    JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE, 2020, 11 (01)
  • [30] Probabilistic models for condition assessment of oil and gas pipelines
    Pandey, MD
    NDT & E INTERNATIONAL, 1998, 31 (05) : 349 - 358