Identification of Apparent Welding Defects Using Computer Vision Based On Deep Learning

Document Type : Original Article

Authors

• Musa Mahmoudi ¹
• soroush ghaderi ¹
• faezeh mahmoudi ²

¹ Department of Structure and Earthquake Engineering, Shahid Rajaee Teacher Training University

² Department of Artificial intelligence, Azad University, Tehran Gharb Branch.

10.22067/jfcei.2022.75044.1118

Abstract

One of the welding controls in health monitoring of structures is to visually control the appearance of welding defects (cracks, Spatter, Overlap, Lack of Fusion). Currently, according to regulations, the appearance quality of welding is controlled by an inspector visually. The accuracy of work in this method depends on the skill level of the inspector. Non using of equipment and technology leads to a high error in identifying visual defects. In this research, a method is proposed to be able to more accurately identify the appearance of welding defects with the help of imaging using machine vision based on deep learning. Convolutional network is used for deep learning to extract features from the image. The results show that the proposed method can identify welding defects with an acceptable accuracy (over 85%). Also, the results show that by using the proposed method, welding defects are evaluated more quickly compared to the traditional method.

Keywords

• appearance welding defects
• Convolutional Neural Network
• image processing
• deep learning
• computer vision

---

References

 [1] Iranian Office of the Deputy for Technical Affairs Bureau of Technical Affairs and Standards, Iranian structural welding code No. 228, Tehran, Center for scientific documents and publications, (2001), (In Persian).

[2] Iranian Office of National Regulations and Building Control, Guide to welding and welded joints in steel buildings, Tehran, Tosseh publisher, (2015), (In Persian).

[3] Fatemi S.M., Visual inspection of welding, Tehran, Keyfiat publisher, (2018), (In Persian).

[4]   Mahmoudi, A., & Regragui, F., "Welding defect detection by segmentation of radiographic images". 2009 WRI World Congress on Computer Science and Information Engineering, Vol. 7, pp. 111-115. IEEE. (2009, March).

[5] Sun, Y., Bai, P., Sun, H. Y., & Zhou, P., "Real-time automatic detection of weld defects in steel pipe", Ndt & E International, 38(7), 522-528, (2005).

[6] Y.J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, O. Büyüköztürk, "Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types", Computer‐Aided Civil and Infrastructure Engineering, 33(9), pp. 731-747, (2018).

[7] A. Krizhevsky, I. Sutskever, G.E. Hinton, "Imagenet classification with deep convolutional neural networks", Advances in neural information processing systems, 25, pp. 1097-1105, (2012).

[8] Y. LeCun, Y. Bengio, "Convolutional networks for images, speech and time series", The handbook of brain theory and neural networks, 3361(10), 1995, (1995).

[9] Y.J. Cha, W. Choi, O. Büyüköztürk, "Deep learning‐based crack damage detection using convolutional neural networks", Computer‐Aided Civil and Infrastructure Engineering, 32(5), pp. 361-378, (2017).

[10] M. Mohtasham Khani, S. Vahidnia, L. Ghasemzadeh, Y.E. Ozturk, M. Yuvalaklioglu, S. Akin, N.K. Ure, "Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines", Structural Health Monitoring, 19(5), pp. 1440-1452, (2020).

[11] C. Feng, H. Zhang, S. Wang, Y. Li, H. Wang, F. Yan, "Structural damage detection using deep convolutional neural network and transfer learning", KSCE Journal of Civil Engineering, 23(10), pp. 4493-4502, (2019).

[12] J. Long, E. Shelhamer, T. Darrell, "Fully convolutional networks for semantic segmentation", IEEE conference on computer vision and pattern recognition, pp. 3431-3440, (2015).

[13] X. Yang, H. Li, Y. Yu, X. Luo, T. Huang, X. Yang, "Automatic pixel‐level crack detection and measurement using fully convolutional network", Computer‐Aided Civil and Infrastructure Engineering, 33(12), 1090, (2018).

[14] L. Zhang, F. Yang, Y.D. Zhang, Y.J. Zhu, "Road crack detection using deep convolutional neural network", 2016 IEEE international conference on image processing (ICIP), IEEE, 2016, pp. 3708-3712.

[15] V. Hoskere, Y. Narazaki, T.A. Hoang, B. Spencer Jr, "MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure", Journal of Civil Structural Health Monitoring, 10, 757-773, (2020).

[16] O. Ronneberger, P. Fischer, T. Brox, "Unet: Convolutional networks for biomedical image segmentation", International Conference on Medical image computing and computerassisted intervention, Springer, pp. 234, (2015).

[17] Z. Liu, Y. Cao, Y. Wang, W. Wang, "Computer vision-based concrete crack detection using U-net fully convolutional networks", Automation in Construction, 104, pp. 129-139, (2019).  

[18] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A.L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution and fully connected crfs", IEEE transactions on pattern analysis and machine intelligence, 40(4), pp. 834-848, (2017).

[19] L.-C. Chen, G. Papandreou, F. Schroff, H. Adam, "Rethinking atrous convolution for semantic image segmentation", arXiv preprint arXiv,1706.05587, (2017).

[20] L.C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, "Encoder-decoder with atrous separable convolution for semantic image segmentation", the European conference on computer vision (ECCV), pp. 801-818, (2018).

[21] Zhang, Z., Wen, G., & Chen, S., "Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding", Journal of Manufacturing Processes, 45, 208-216, (2019).

[22] Feng, S., Zhou, H., & Dong, H., "Using deep neural network with small dataset to predict material defects", Materials & Design, 162, 300-310, (2019).

[23] B. Zoph, V. Vasudevan, J. Shlens, Q.V. Le, "Learning transferable architectures for scalable image recognition", IEEE conference on computer vision and pattern recognition, pp. 8697-8710, (2018).

[24] Zhang, Y., You, D., Gao, X., Zhang, N., & Gao, P. P., "Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates", Journal of Manufacturing Systems, 51, 87-94, (2019).

[25] Chollet, F., "Deep Learning with Python", United States: Manning Publications, (2018).

[26] Hou, W., Wei, Y., Guo, J., & Jin, Y., "Automatic detection of welding defects using deep neural network" Journal of Physics: Conference Series, Vol. 933, No. 1, p. 012006, IOP Publishing, (2017, December).

[27] Deng, L., "A tutorial survey of architectures, algorithms, and applications for deep learning", APSIPA Transactions on Signal and Information Processing, (2014).