An Improved Image Filtering Method for Weld Bead Inspection using Unsharp Masking Technique

N. Awang, M.H.F. Md Fauadi, A.Z.M. Noor, S.A. Idris, N.S. Rosli

Abstract


There are many disturbances that occur during image capturing process. One of the common disturbances is noise. Consequently, various methods were developed to improve image quality. In this study, the proposed method consists of an enhanced Unsharp Masking Technique that is combined with common filtering methods. The method was applied in different noise situations. The image filtering methods involved were common filter which Mean, Median and Gaussian filters. The images of welding process were converted into RGB for ease of calculation. Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) were used to determine the quality of the image. Graph generated to reinforce the PSNR value. The results obtained proved that the proposed method could yield better filtering performance.

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References


R.J. Silva, G.F. Barbosa and J. Carvalho, “Additive Manufacturing of Metal Parts by Welding,” in IFAC-International Federation of Automatic Control, vol. 48, no. 3, pp. 2318–2322, 2015.

S. Schmid and S. Kalpakjian, Manufacturing Engineering & Technology, 7th Edition. USA: Prentice Hall, 2013.

G.S. Kumar, U. Natarajan and S.S. Ananthan, “Vision Inspection System For The Identification and Classification of Defects in MIG Welding Joints,” The International Journal of Advanced Manufacturing Technology, vol. 61, no. 9–12, pp. 923–933, 2012.

Y. Li, Y.F. Li, Q.L. Wang, D. Xu and M. Tan, “Measurement and Defect Detection of The Weld Bead Based on Online Vision Inspection,” in IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 7, pp. 1841–1849, 2010.

S. Chaki, B. Shanmugarajan, S. Ghosal, and G. Padmanabham, “Application of Integrated Soft Computing Techniques for Optimisation Of Hybrid CO 2 laser–MIG welding process,” Applied Soft Computing, vol. 30, pp. 365-374, 2015.

N.B. Yahia, T. Belhadj, S. Brag and A. Zghal, “Automatic Detection of Welding Defects using Radiography with a Neural Approach,” Procedia Engineering, vol. 10, pp. 671–679, 2011.

J. Hassan, M. Awan, and A. Jalil, “Welding Defect Detection and Classification Using Geometric Features,” in International Conference on Frontiers of Information Technology, Islamabad, 2012, pp. 139–144.

Z. Shen and J. Sun, “Welding Seam Defect Detection for Canisters Based on Computer Vision,” in International Congress on Image and Signal Processing, Hangzhou, 2013, pp. 788–793.

D. Bracun and A. Sluga, “Stereo Vision Based Measuring System For Online Welding Path Inspection,” Journal of Materials Processing Technology, vol. 223, pp. 328–336, 2015.

M. Sonka, H. Vaclav and B. Roger, Image Processing, Analysis, and Machine Vision, Cengage Learning. USA: Springer, 2014.

V. S. Rathore and V. Dubey, “A Study to Improve The Quality of Image Enhancement Based on Different Filtering Design Techniques,” International Journal of Research in Engineering and Technology, vol. 2, no. 8, pp. 11–15, 2013.

R. Ranjan, A.R. Khan, C. Parikh, R. Jain, R.P. Mahto, S. Pal, S.K. Pal, and D. Chakravarty, “Classification and Identification of Surface Defects In Friction Stir Welding: An Image Processing Approach,” Journal of Manufacturing Processes, vol. 22, pp. 237–253, 2016.

R. Verma and J. Ali, “A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 10, pp. 617–622, 2013.

H. F. Fauadi, M. H. Nordin and Z. M. Zainon, “Frontal obstacle avoidance of an autonomous subsurface vehicle (ASV) using fuzzy logic method,” in International Conference of Intelligence Advance System, 2007, pp. 125–128.

P. Kamboj and V. Rani, “A Brief Study of Various Noise Model and Filtering Techniques,” Journal of Global Research in Computer Science, vol. 4, no. 4, pp. 166–171, 2013.

M.H.F.b.M. Fauadi, H. Lin and T. Murata, "Dynamic task assignment of autonomous AGV system based on multi agent architecture," 2010 IEEE International Conference on Progress in Informatics and Computing, Shanghai, 2010, pp. 1151-1156.

C.T. Lu and T.C. Chou, “Denoising of Salt-and-Pepper Noise Corrupted Image Using Modified Directional-Weighted-Median Filter,” Pattern Recognition Letters, vol. 33, no. 10, pp. 1287–1295, 2012.

M.H.F. bin Md Fauadi and T. Murata, “Makespan Minimization of Machines and Automated Guided Vehicles Schedule Using Binary Particle Swarm Optimization.”Proceedings of the International Multiconference of Engineers and Computer Scientists (IMECS 2010) Vol. 3, 2010, pp1897-1902.

Y. Zou, D. Du, B. Chang, L. Ji, and J. Pan, “Automatic Weld Defect Detection Method Based on Kalman Filtering For Real-Time Radiographic Inspection of Spiral Pipe,” NDT & E International, vol. 72, pp. 1–9, 2015.

M.H.F.B.M. Fauadi, W.L. Li and T. Murata, “Combinatorial auction method for decentralized task assignment of multiple-loading capacity AGV based on intelligent agent architecture,” in IEEE 2nd International Conference of Innovation Bio-Inspired Computer Application, 2011, pp. 207–211.

C.Y. Chen, C.H. Chen, and K.P. Lin, “An Automatic Filtering Convergence Method For Iterative Impulse Noise Filters Based on PSNR Checking and Filtered Pixels Detection,” Expert Systems with Applications, vol. 63, pp. 198–207, 2016.

N. Awang, M.H.F.M. Fauadi and N.S. Rosli, “Image Processing of Product Surface Defect Using Scilab," Applied Mechanics and Materials,” vol. 789-790, pp. 1223-1226, 2015.

S.H. Kim and J.P. Allebach, "Optimal Unsharp Mask For Image Sharpening and Noise Removal," Journal of Electronic Imaging, vol. 14, no. 2, pp. 1-13, 2005.




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