Recognition algorithm of small-diameter tube X-ray welding defect image

XIAO Yang; GAO Weixin; DENG Guohao

doi:10.12073/j.hjxb.20230228001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2024 > 45(2): 82-88. > DOI: 10.12073/j.hjxb.20230228001

XIAO Yang, GAO Weixin, DENG Guohao. Recognition algorithm of small-diameter tube X-ray welding defect image[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(2): 82-88. DOI: 10.12073/j.hjxb.20230228001

Citation:

PDF (2547 KB)

Recognition algorithm of small-diameter tube X-ray welding defect image

XIAO Yang^1,,
GAO Weixin^{1, 2, ,},
DENG Guohao²

1.
School of Electronic Engineering, Xi’an Shiyou University, Xi’an, 710065, China
2.
Key Laboratory of Gas-Oil Logging Technology, Xi’an Shiyou University, Xi’an, 710065, China

More Information

Received Date: February 27, 2023
Available Online: January 14, 2024

Graphical Abstract

Abstract

Abstract

To address the current situation of low accuracy rate of small-diameter tube welding image defect detection, by combining image feature analysis and sparse dictionary learning, a small-diameter tube welding defect detection algorithm based on image segmentation is proposed. Firstly, using two-step image segmentation way acquires the region of interest which is in small-diameter tube welding image. Secondly, the suspected defect region is obtained by extracting welding defect. Finally, we propose a mathematical model of the dictionary matrix of small-diameter tube welding defects with the objective of minimizing correlations between different types of atoms and solve it by using K-SVD algorithm. After that, the dictionary matrix is used to classify circular defects, strip defects and noise. To improve the real-time performance of the system, we use parallel programming to accelerate the image segmentation algorithm. The results show that the recognition rate of the proposed method is 0.974 for circular defects and 0.967 for strip defects, and the recognition speed is fast, which enables the effective recognition of defects in small-diameter tube welding image.
- small-diameter tube,
- welding defect,
- image segmentation,
- sparse dictionary learning

FullText(HTML)

References (15)

References

[1]	周兆逸, 张亚南, 王肖锋, 等. 基于改正二维主成分分析的焊缝表面缺陷检测[J]. 焊接学报, 2021, 42(11): 70 − 76. Zhou Zhaoyi, Zhang Yanan, Wang Xiaofeng, et al. Weld surface defect detection based on improved two dimensional principal c-omponent analysis[J]. Transactions of the China Welding Institution, 2021, 42(11): 70 − 76.
[2]	Yang L, Wang H X, Huo B Y, et al. An automatic welding defect location algorithm based on deep learning[J]. NDT & E International:Independent Nondestructive Testing and Evaluation, 2021, 120: 102435.
[3]	Sun J, Li C, Wu X J, et al. An Effective method of weld defect detection and classification based on machine vision[J]. Ieee Transactions on Industrial Informatics, 2019, 15(12): 6322 − 6333. doi: 10.1109/TII.2019.2896357
[4]	Zhang Y, You D, Gao X, et al. Welding defects detectionbased on deep learning with multiple optical sensors during disklaser welding of thick plates[J]. Journal of Manufacturing Systems, 2019, 51: 87 − 94. doi: 10.1016/j.jmsy.2019.02.004
[5]	魏博文, 高炜欣, 刘梦溪. 激光超声结合CNN的铸件缺陷检测方法[J]. 激光与红外, 2022, 52(9): 1327 − 1334. doi: 10.3969/j.issn.1001-5078.2022.09.009 Wei Bowen, Gao Weixin, Liu Mengxi. Casting defect detection method combined with laser ultrasound and convolutional neural network[J]. Laser & Infrared, 2022, 52(9): 1327 − 1334. doi: 10.3969/j.issn.1001-5078.2022.09.009
[6]	Zhang L, Zhang Y J, Dai B C, et al. Welding defect detectionbased on local image enhancement[J]. IET Image Processing, 2019, 13(13): 2647 − 2658. doi: 10.1049/iet-ipr.2018.5840
[7]	江野, 王东, 秦小阳, 等. 奥氏体异种钢薄壁小径管焊缝裂纹超声相控阵检测[J]. 焊接学报, 2018, 39(8): 119 − 122. Jiang Ye, Wang Dong, Qin Xiaoyang, et al. Ultrasonic phased array inspection on welded crack of austenitic dissimilar ateel with thinwall and small diameter tube[J]. Transactions of the China Welding Institution, 2018, 39(8): 119 − 122.
[8]	陈小明, 谭云华, 赖传理, 等. 相控阵技术在超小径管焊缝检测应用研究[J]. 锅炉技术, 2021, 52(S1): 55 − 58. Chen Xiaoming, Tan Yunhua, Lai Chuanli, et al. The reserch of phased array testing on the butt weld of tube[J]. Boiler Technology, 2021, 52(S1): 55 − 58.
[9]	Xie L, Wang T, Zhang Y. Comparative experimental study on phased array and X-ray detection of small diameter pipe weld[J]. Journal of Physics, 2021, 1885(3): 032023.
[10]	贾潇, 赵隆. 相控阵超声与射线检测对石油化工管道缺陷检测的对比分析[J]. 中国石油和化工标准与质量, 2022, 42(15): 174 − 175. Jia Xiao, Zhao Long. Comparative analysis of ultrasonic phasedarray and X-ray detection for petrochemical pipeline defect dete-ction[J]. China Petroleum and Chemical Standard and Quality, 2022, 42(15): 174 − 175.
[11]	张小琳, 刘祎, 白贇沨, 等. 基于背景估计的焊缝缺陷检测[J]. 电子测量技术, 2022, 45(14): 116 − 122. Zhang Xiaolin, Liu Yi, Bai Yunfeng, et al. Weld defect detectionbased on background estimation[J]. Electronic Measurement Technology, 2022, 45(14): 116 − 122.
[12]	高炜欣, 胡玉衡, 穆向阳, 等. 基于聚类的埋弧焊X射线焊缝图像缺陷分割算法及缺陷模型[J]. 焊接学报, 2012, 33(4): 37 − 41. Gao Weixin, Hu Yuheng, Mu Xiangyang, et al. Study on sub-arc X-ray welding image defect segmentation algorithm and defectmodel[J]. Transactions of the China Welding Institution, 2012, 33(4): 37 − 41.
[13]	孙春红, 丁广太, 方坤. 基于稀疏字典学习的羊绒与羊毛分类[J]. 纺织学报, 2022, 43(4): 28 − 32. Sun Cunhong, Ding Guangtai, Fang Kun. Cashmere and wool classification based on sparse dictionary learning[J]. Journal of Textile Research, 2022, 43(4): 28 − 32.
[14]	Hou W H, Zhang D S, Wei Y, et al. Review on computer aided weld defect detection from radiography images[J]. Applied Sciences, 2020, 10(5): 1878 − 1893. doi: 10.3390/app10051878
[15]	修延飞, 李海超, 胡广泽, 等. 一种用于穿孔塞焊焊缝特征提取的视觉识别算法[J]. 焊接学报, 2020, 41(2): 75 − 79. doi: 10.12073/j.hjxb.20190927002 Xiu Yuanfei, Li Haichao, Hu Guangze, et al. A visual recognition algorithms for seam feature extraction of perforated plug welding[J]. Transactions of the China Welding Institution, 2020, 41(2): 75 − 79. doi: 10.12073/j.hjxb.20190927002

[1]	ZHU Ming, LEI Runji, WENG Jun, WANG Jincheng, SHI Yu. MIG weld seam tracking system based on image automatic enhancement and attention mechanism deep learning[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 90-94. DOI: 10.12073/j.hjxb.20240718002
[2]	XU Yuanzhao, LUO Jiutian, FANG Naiwen, FENG Zhiqiang, WU Pengbo, LI Quan. Image processing technology for ship plate melt pool based on MS-FCM algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(3): 82-90. DOI: 10.12073/j.hjxb.20231010001
[3]	DAI Xinxin, ZHENG Qiaoqiao, JI Yukun, GAO Xiangdong. Numerical simulation and experimental analysis of magnetic field distribution of magneto-optical imaging in weld defects[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(12): 102-108. DOI: 10.12073/j.hjxb.2019400321
[4]	CHI Dazhao, GANG Tie. Defect detection method based on 2D entropy image segmentation[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(12): 25-28.
[5]	QI Jiyang, LI Jinyan, LU Zhenyun, WEI Sai. Application of improved Otsu algorithm to welding image segmentation[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(10): 97-100.
[6]	GAO Weixin, HU Yuheng, MU Xiangyang, WANG Zhi. Study on sub-arc X-ray welding image defect segmentation algorithm and defect model[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2012, (4): 37-41.
[7]	SHEN Yingji, WANG Kehong, PAN Mingcai, CHEN Fei. Prelimimary about pool image character of welding defect based on vision sensing[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2012, (1): 105-108.
[8]	LIU Suyi, LI Bing, ZHANG Hua, JIA Jianping. Feature extraction and image processing for underwater weld with laser vision[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (9): 45-48.
[9]	SHAO Jiaxin, DU Dong, ZHU Xinjie, GAO Zhiling, WANG Chen. Weld defect detection of double sides weld based on X-ray digitized image[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (11): 21-24.
[10]	SUN Yi, SUN Hong-yu, BAI Peng, WANG Yu, TIAN Yan-ping. Real-time automatic detection of weld defects in X-ray images[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2004, (2): 115-118,122.

Cited By

Cited by

Periodical cited type(4)

1.	朱新杰，李永涛，邓明晰，姚森，张洁. 焊缝散射条件下板中超声导波直线阵列多帧变秩成像检测. 焊接学报. 2025(01): 80-86 . 本站查看
2.	宋艳艳，王泽民，叶振宇，陈乐. 基于视觉软件的小径管对接焊缝DR图像缺陷自动识别. 上海化工. 2025(02): 44-48 .
3.	李兴红. 基于深度学习的液氯罐车射线图像缺陷自动识别研究. 中国高新科技. 2024(12): 15-17 .
4.	程希莹，何云凯，吴晗宇. 基于深度学习的焊缝X射线图像缺陷识别. 淮阴师范学院学报(自然科学版). 2024(04): 319-328 .

Other cited types(0)

Get Citation

PDF

XML

Article views (133) PDF downloads (32) Cited by(4)

Turn off MathJax

Article Contents

Abstract

References

Recognition algorithm of small-diameter tube X-ray welding defect image