IMAGE ENHANCEMENT IN INDUSTRIAL WELDING ENVIRONMENT WITH IMAGE PROCESSING TECHNIQUES

Year 2025, Volume: 13 Issue: 1, 238 - 259, 01.03.2025

Levent Civcik , Muhammed Alperen Aksin

https://doi.org/10.36306/konjes.1434797

Abstract

With the increase and acceleration of production capacity, computer-based control mechanisms are becoming increasingly common in industrial applications. The use of intelligent welding robots in the welding industry is increasing due to their instant decision-making and application capabilities. For this reason, computer vision systems and image processing algorithms are increasingly used. Although visual limitations in sensors and industrial environmental conditions (arc, noise, dust, etc.) cause problems in robotic welding applications, computer-controlled systems achieve much more efficient results than operator-controlled systems.
One of the most important points here is the applicability and stability of the algorithm to the system. In this study, considering the computational load of image processing algorithms and the negative effects of this computational load in moving environments, a more stable and efficient image feature extraction algorithm was tried to be created for robotic welding applications. After the welding process, object recognition was performed by performing object feature matching with the help of samples taken from the weld images. A new algorithm was created to recognize welding processes that differ from each other in some aspects with multiple samples and even to detect different types of welds. This algorithm reduces the images to gray level and performs a pre-processing step to remove noise with a filtering process, then detects the weld points with the help of predetermined templates and decides how accurately these points are made. Thanks to the NCC Template Matching method used in the algorithm, the running time of the algorithm is accelerated and more accurate results are obtained by introducing more than one template
Experimental method aimed both to calculate the accuracy rate in case the same type of weld operations are different from each other and to recognize the operations performed with different types of welds. While the detection level was around 60% in images without image preprocessing, the detection rate exceeded 70% in images with image preprocessing. In the experiments conducted on the images taken with the Template Matching algorithm, it was observed that the detection rate increased to around 75% at different threshold values. In addition, with the region of interest selection and NCC method, the running time of the algorithm was reduced to 190 ms on average.
Considering the results obtained in the experiments, the algorithm significantly improved the accuracy rates of spot welding and the differentiation of different weld types. By using sufficient light welds and correct experimental equipment, the success rate of the Template Matching algorithm has been increased and the processing load has been alleviated. The effect of external environmental conditions, which is considered the biggest disadvantage of the algorithm, is minimized with lighting elements.

Keywords

Object Recognition Based Template Matching, Image Filters, Full Frame Method, Pattern Matching Method, Industrial Welding Process

References

• Lei Yang, Yanhong Liu & Jinzhu Peng, “Advances techniques of the structured light sensing in intelligent welding robots: a review,” The International Journal of Advanced Manufacturing Technology, 110:1027–1046, 2020.
• Runquan Xiao, Yanling Xu, Zhen Hou, Chao Chen, Shanben Chen, “An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding,” Sensors and Actuators A: Physical 297, 111533, 2019.
• Pires JN, Loureiro A, Bölmsjo G, “ Welding robots: technology, system issues and Application,” Springer Science & Business Media, 10.1007/1-84628-191-1, 2006.
• Cook GE, Andersen K, Fernandez KR, Shepard ME, Wells Jr AM, “Electric arc sensing for robot positioning control,”, Robotic Welding, IFS(Publications) Ltd, 181–216, 1987.
• Fridenfalk M, “Development of intelligent robot systems based on sensor control,” Lund University, 2003.
• Brahim, B.S., Josefina J. and Nourain, N., “Fast Template Matching Method based Optimized Sum of Absolute Difference Algorithm for Face Localization”. International Journal of Computer Applications (IJCA), Mart. http://eprints.utp.edu.my/4685/, 2011.
• Saravanan, C. and Surender, M. “Algorithm for Face Matching Using Normalized Cross-Correlation,” International Journal of Engineering and Advanced Technology (IJEAT) ISSN, 2249–8958, 2013.
• Jawad Muhammad & Halis Altun & Essam Abo-Serie, “Welding seam profiling techniques based on active vision sensing for intelligent robotic welding,” Int J Adv Manuf Technol, 88:127–145, 2017.
• Rongqiang Du1 & Yanling Xu1 & Zhen Hou1 & Jun Shu2 & Shanben Chen, “Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding,” The International Journal of Advanced Manufacturing Technology, 101:2135–2149, 2019.
• Maini, R. and Aggarwal, H., “A Comprehensive Review of Image Enhancement Techniques,” Journal of computıng, volume 2, ıssue 3, ıssn 2151-9617, 2010.
• Ferrari, V., Tuytelaars, T. and Gool, L. V., “Simultaneous Object Recognition and Segmentation by Image Exploration,” Computer Vision – ECCV, Tomás Pajdla ve Jiří Matas, 40–54. Lecture Notes in Computer Science 3021. Springer Berlin Heidelberg, 2004. [Online]. Available: http://link.springer.com/chapter/10.1007/978-3-540-24670-1_4. [Accesed July 27, 2023]
• Zhu, S. and Ma, K.K.., “A new diamond search algorithm for fast block matching motion Estimation,” IEEE Transactions on Image Processing 9 (2): 287–90. doi:10.1109/83.821744, 2000.
• Aslıhan, M., “Nesneyi Temel Düzeyde Tespit Edebilme (Template Matching) Aşamaları,”, 2020. [Online]. Available: https://medium.com/kodcular/nesneyi-temel-düzeyde-tespit-edebilme-template-matching-aşamaları-6e11f8bd0a0d [Accesed July 25, 2023]
• Kettnaker, V. and Zabih, R. 1999. “Bayesian multi-camera surveillance”. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on., 2:-259 Vol. 2. doi: 10.1109/CVPR.1999.784638, 1999.
• Ali Değirmenci, İlyas Çankaya, Recep Demirci, 2018, “Gradyan Anahtarlamalı Gauss Görüntü Filtresi”, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 6, 196-215, 2018.
• Hanna G., “Object Tracking. Hard Cover”, Publisher: In Tech, Subject: Artificial Intelligence, pp: 284, ISBN: 978-953-307-360-6, 2011.
• Yılmaz, A., Javed, O. and Shah, M., “Object Tracking: A Survey”. ACM Comput. Surv. 38 (4). doi:10.1145/1177352.1177355, 2006.
• Xu, R.Y.D., Allen, J.G. and Jin, J.S., “Robust Real-time Tracking of Non-rigid Objects”. Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing, 95–98. VIP ’05, 2004.
• Fang Z, Xu D, Tan M, “Vision-based initial weld point positioning using the geometric relationship between two seams.” Int J Adv Manuf Technol, 66(9–12):1535–1543, 2013.
• Sahani, S.K., Adhikari, G. and Das, B.K. 2011. “A fast template matching algorithm for aerial object tracking,” International Conference on Image Information Processing (ICIIP), 1–6, 2011. doi: 10.1109/ICIIP.2011.6108841.
• Dian Ary, Nurul Muhayat, Triyono, “Example Assessment Shielded Metal Arc Welding,” E3S Web of Conferences 465, 01012, PST.04.04.07.b., 2023. [Online]. Available: www.isbe.net/assessment/htmls/balanced-asmt.htm , Agriculture, Grade 9-12 [Accesed September 18, 2024]
• M. A. Aksin, “Kaynak Robotlarında Şablon Eşleştirme Algoritmasının Kullanımı”, Institute of Graduate Studies Konya Technical University, Electric Electronic Engineering, Konya, 2023.