Automated Visual Inspection Model For Screw Detection on The Moving Objects In Industrial Quality Control
DOI:
https://doi.org/10.61841/4vt3t192Keywords:
Machine Learning, Computer vision, , Vision control,, TemplateAbstract
Modern industrial automated visual inspection models provide new methods and solutions in industrial visual quality control application. In this paper, we present automated visual inspection (AVI) model based on the normalized cross-correlation template matching algorithm for real- time screw detection on a moving object. The model can recognize products and detect screws on moving objects. We present the implementation challenges and provide guidelines for successful industrial application. We present experimental results, and discuss model constraints, and implementation parameters settings, showing that the proposed model is very sensitive both to object distance from the camera and a small rotation of the object during template detection on moving objects. The problem of vibrations in real industrial environments and our proposed solution that signi_cantly improves model accuracy is also presented. Our findings show that the model can outperform humans in visual quality control process.
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[1] Bhandarkar, S. M., Faust, T. D., & Tang, M. (1999). Catalog: a system for detection and rendering of internal log defects using computer tomography. Machine Vision and pplications, 11 (4), 171–190.
[2] Bradski, G. & Kaehler, A. (2000). Opencv. Dr. Dobb’s journal of software tools, 3.
[3] Cruz-Ramirez, S. R., Mae, Y., Takubo, T., & Arai, T. (2008). Detection of screws on metal-ceiling structures for dismantling tasks in buildings. In Intelligent robots and systems, 2008. iros 2008. ieee/rsj international conference on
[4] (pp. 4123–4129). IEEE.
[5] Davies, E. (1998). Automated visual inspection. Machine Vision, 471–502.
[6] Drury, C. (1992). Inspection performance. Handbook of industrial engineering, 2282–2314.
[7] Grand d’Esnon, A. (1987). Magali: a self-propelled robot to pick apples. ASAE Paper No. 87-1037.
[8] Harrell, R., Adsit, P. D., Munilla, R., & Slaughter, D. (1990). Robotic picking of citrus. Robotica, 8 (4), 269– 278.
[9] Harrell, R., Slaughter, D., & Adsit, P. D. (1989). A fruit-tracking system for robotic harvesting. Machine Vision and Applications, 2 (2), 69–80.
[10] Heipke, C. (1996). Overview of image matching techniques. In Oeepe workshop on the application of digital photogrammetric workstations, 1996.
[11] Juste, F. & Sevila, F. (1992). Citrus: an european project to study the robotic harvesting of oranges. Rapport. Institut for Jordbrugsvidenskab (Denmark). no. 67.
[12] Kuruppu,G., Manoj, C., Kodituwakku, S., & Pinidiyaarachchi, U. (2013). Comparison of different template matching algorithms in high speed sports motion tracking. In Industrial and information systems (iciis), 2013 8th ieee international conference on (pp. 445–448). IEEE.
[13] Li, H. & Lin, J. C. (1994). Using fuzzy logic to detect dimple defects of polished wafer surfaces. IEEE transactions on industry applications, 30 (2), 317–323.
[14] Malamas, E. N., Petrakis, E. G., Zervakis, M., Petit, L., & Legat, J.-D. (2003). A survey on industrial vision systems, applications and tools. Image and vision computing, 21 (2), 171–188.
[15] Perveen, N., Kumar, D., & Bhardwaj, I. (2013). An overview on template matching methodologies and its applications. IJRCCT, 2 (10), 988–995.
[16] Pesante-Santana, J. & Woldstad, J. C. (2000). Quality inspection task in modern manufacturing. Rabatel, G., Bourely, A., Sevila, F., & Juste, F. (1995). Robotic harvesting of citrus: state-of-art and development of the french spanish eureka project. In Proc. international conference on harvest and post harvest technologies for fresh fruits and vegetables (Vol. 95, pp. 232–239).
[17] Welch, G. & Bishop, G. (2004). An introduction to the kalman filter [ol].
[18] Zhao, F., Huang, Q., & Gao, W. (2006). Image matching by normalized cross-correlation. In Acoustics, speech and signal processing, 2006. icassp 2006 proceedings. 2006 ieee international conference on (Vol. 2, pp. II–II). IEEE.
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