Automatic detection of blowholes defects in X-ray images of thick steel pipes
CHEN Benzhi1, FANG Zhihong2, XIA Yong2, ZHANG Ling3, LAN Shouren1, WANG Lisheng1
1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Research Institute, Baoshan Iron & Steel Company Limited, Shanghai 201900, China; 3. Steel Bars Division, Baoshan Iron & Steel Company Limited, Shanghai 201900, China
Abstract：Due to the intensity distribution of X-ray image of thick steel pipe is not uniform, the contrast is low, the noise is big, and the size, shape, position and contrast of the blowholes defects are different, it is difficult to detect various types of blowholes automatically. Aiming at the problems that the traditional defect detection algorithm has a large workload of manually marking defect data, and the edge of the weld is difficult to accurately extract and other issues, a new unsupervised learning algorithm was proposed for the detection of various blowholes defects. Firstly, fast Independent Component Analysis (ICA) was used to learn a set of independent base vectors from the steel pipe X-ray image set, and a linear combination of the base vectors was used to selectively reconstruct the test image with blowholes defect. Then, the test image was subtracted from its reconstructed image to obtain the difference image, and the various blowholes were separated from the difference image by global threshold. There were 320 images in the training set and 60 images in the test set. The average sensitivity and accuracy of the proposed algorithm were 90.5% and 99.7%. The experimental results show that the algorithm can accurately detect all kinds of blowholes defects without manual marking the data or extracting the edge of the weld.
[1] 高炜欣,胡玉衡,武晓朦,等.埋弧焊X射线焊缝缺陷图像分类算法研究[J].仪器仪表学报,2016,37(3):518-524.(GAO W X, HU Y H, WU X M, et al. Sub-arc X-ray welding defect image classifying algorithm[J]. Chinese Journal of Scientific Instrument, 2016,37(3):518-524.) [2] 李雪琴,刘培勇,殷国富,等.基于Fourier拟合曲面的X射线焊缝缺陷检测[J].焊接学报,2014,35(10):61-64.(LI X Q, LIU P Y, YIN G F, et al. Weld defect detection by X-ray images method based on Fourier fitting surface[J]. Transactions of the China Welding Institution, 2014, 35(10):61-64.) [3] 胡海林,李静,李剑,等.基于视觉与电弧多信息传感的焊缝跟踪算法[J].计算机应用,2012,32(6):1760-1765.(HU H L, LI J, LI J, et al. Seam tracking algorithm based on multi-information sensor of vision and arc[J]. Journal of Computer Applications, 2012, 32(6):1760-1765.) [4] DANG C Y, GAO J M, WANG Z, et al. A novel method for detecting weld defects accurately and reliably in radiographic images[J]. Insight-Non-Destructive Testing and Condition Monitoring, 2016, 58(1):28-34. [5] RATHOD V R, ANAND R S. A comparative study of different segmentation techniques for detection of flaws in NDE weld images[J]. Journal of Nondestructive Evaluation, 2012, 31(1):1-16. [6] SHAH M, NIZAM H, RASHID A, et al. Vision based identification and classification of weld defects in welding environments:a review[J]. Indian Journals of Science and Technology, 2016, 9(20):1-15. [7] MOGHADDAM A A, RANGARAJAN L. Classification of welding defects in radiographic images[J]. Pattern Recognition and Image Analysis, 2016, 26(1):54-60. [8] ZAPATA J, VILAR R, RUIZ R. An adaptive-network-based fuzzy inference system for classification of welding defects[J]. NDT and E International, 2010, 43(3):191-199. [9] VALAVANIS I, KOSMOPOULOS D. Multiclass defect detection and classification in weld radiographic images using geometric and texture features[J]. Expert Systems with Applications, 2010, 37(12):7606-7614. [10] ZAPATA J, VILAR R, RUIZ R. Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers[J]. Expert Systems with Applications, 2011, 38(7):8812-8824. [11] CHANG J, LIU W B, HU H, et al. Improved independent component analysis based modal identification of higher damping structures[J]. Measurement, 2016, 88:402-416. [12] REYHANI N, YLIPAAVALNIEMI J, VIGÁRIO R, et al. Consistency and asymptotic normality of FastICA and bootstrap FastICA[J]. Signal Processing, 2012, 92(8):1767-1778. [13] SHYU K K, LEE M H, WU Y T, et al. Implementation of pipelined FastICA on FPGA for real-time blind source separation[J]. IEEE Transactions on Neural Networks, 2008, 19(6):958-970.
2017, Vol. 37 
