Automatic weld defect identification from radiographic images

被引：80

作者：

Zahran, O. ^{[1
]}

Kasban, H.

El-Kordy, M. ^{[1
]}

Abd El-Samie, F. E. ^{[1
]}

机构：

[1] Menoufia Univ, Fac Elect Engn, Dept Elect & Elect Commun, Menoufia 32952, Egypt

来源：

NDT & E INTERNATIONAL | 2013年 / 57卷

关键词：

Radiographic images; Feature extraction; Power density spectrum (PDS); Weld defect detection; NDT SYSTEM; CLASSIFICATION; INSPECTION;

D O I：

10.1016/j.ndteint.2012.11.005

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

This paper presents a new approach for weld defect identification from radiographic images. This approach is based on the generation of a database of defect features using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients extracted from the Power Density Spectra (PDSs) of the weld segmented areas after performing pre-processing and segmentation stages. Artificial Neural Networks (ANNs) are used for the feature matching process in order to automatically identify defects in radiographic images. The performance of the proposed approach is evaluated using 150 radiographic images in the presence of various types of noise and blurring. The experimental results show that the proposed approach can be used in a reliable way for automatic weld defect identification from radiographic images in noisy environments, and can achieve high recognition rates. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：26 / 35

页数：10

共 50 条

[21] Weld Defect Detection From Imbalanced Radiographic Images Based on Contrast Enhancement Conditional Generative Adversarial Network and Transfer Learning
Guo, Runyuan
Liu, Han
Xie, Guo
Zhang, Youmin
IEEE SENSORS JOURNAL, 2021, 21 (09) : 10844 - 10853
[22] A Deep Learning-Based Weld Defect Classification Method Using Radiographic Images With a Cylindrical Projection
Chang, Yasheng
Wang, Weiku
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
[23] A Deep Learning-Based Weld Defect Classification Method Using Radiographic Images with a Cylindrical Projection
Chang, Yasheng
Wang, Weiku
IEEE Transactions on Instrumentation and Measurement, 2021, 70
[24] Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers
Zapata, Juan
Vilar, Rafael
Ruiz, Ramon
EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (07) : 8812 - 8824
[25] Automatic defect identification using thermal image analysis for online weld quality monitoring
Sreedhar, U.
Krishnamurthy, C. V.
Balasubramaniam, Krishnan
Raghupathy, V. D.
Ravisankar, S.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2012, 212 (07) : 1557 - 1566
[26] Review on Computer Aided Weld Defect Detection from Radiography Images
Hou, Wenhui
Zhang, Dashan
Wei, Ye
Guo, Jie
Zhang, Xiaolong
APPLIED SCIENCES-BASEL, 2020, 10 (05):
[27] Automatic Evaluation of Digital Radiographic Images
Das, S.
Mukherjee, D.
Shah, B. K.
WELDING JOURNAL, 2011, 90 (11) : 29 - 31
[28] Flaws Classification using ANN for Radiographic Weld Images
Kumar, Jayendra
Anand, R. S.
Srivastava, S. P.
2014 INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND INTEGRATED NETWORKS (SPIN), 2014, : 145 - 150
[29] Weld defect classification in radiographic images using unified deep neural network with multi-level features
Lu Yang
Hongquan Jiang
Journal of Intelligent Manufacturing, 2021, 32 : 459 - 469
[30] Weld defect classification in radiographic images using unified deep neural network with multi-level features
Yang, Lu
Jiang, Hongquan
JOURNAL OF INTELLIGENT MANUFACTURING, 2021, 32 (02) : 459 - 469

← 1 2 3 4 5 →