Surface inspection is one of the most important facets of quality-control systems in the steel manufacturing and processing industry. A number of surface defects can be detected by a visual inspection. However, human visual inspection becomes a hard task due to the high-processing speed. In the present paper, the automated visual inspection of flat steel is approached. A detailed description is given of the main aspects involved, concerning image acquisition, image processing algorithms, architecture design, the custom software developed, and data transmission and synchronization. Particular attention is paid to feature extraction and classification. Six kinds of defects are finely classified: weld, white rust, transporter marks, pitting corrosion, protuberance of zinc, and rolled marks. The proposed solution has been implemented and tested in a real industrial environment, in a flat steel cutting factory, showing suitable results. A successful classification ratio of about 87% of the known defects has been obtained, which is considered a reliable result.
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Christmas I (2008) Steel’s climate change commitment. World Steel Association. http://www.worldsteel.org/?action=storypages&id=294. Accessed 15 July 2010
Gonvarri Group (2009) Processes. http://www.gonvarri.com/en/procesos/procesos.asp. Accessed 30 October 2009
Stahleisen (1996) Surface defects in hot rolled flat steel products. Stahleisen, Duesseldorf
Gayubo F, González JL, de la Fuente E, Miguel F, Perán JR (2006) On-line machine vision system for detect split defects in sheet-metal forming processes. Conference on Pattern Recognition (ICPR 2006), Vol. 1, pp. 723–726
Nishibe K, Fujiwara N (1999) Development of automatic steel coil recognition system for automated crane. Proceedings of the 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 742–746
Piuri V, Scotti F, Roveri M (2005) Computational intelligence in industrial quality control. IEEE International Workshop on Intelligent Signal Processing, pp. 4–9
Tomczac L, Mosorov V, Sankowski D, Nowakowski J (2007) Image defect detection methods for visual inspection systems. 9th International Conference—The Experience of Designing and Applications of CAD Systems in Microelectronics (CADSM’07), pp. 454–456.
Acciani G, Brunetti G, Fornarelli G (2006) Application of neural networks in optical inspection and classification of solder joints in surface mount technology. IEEE Trans Ind Inf 2(3):200–209
Jia H, Murphey YL, Shi J, Chang TS (2004) An intelligent real-time vision system for surface defect detection. Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), Vol. 3, pp. 239–242
Kang GW, Liu HB (2005) Surface defects inspection of cold rolled strips based on neural network. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vol. 8, pp. 5034–5037
Sharifzadeh M, Alirezaee S, Amirfattahi R, Sadri S (2008) Detection of steel defect using the image processing algorithms. IEEE International Multitopic Conference, pp. 125–127
ISRA VISION Parsytec (2009) http://www.parsytec.de/. Accessed 15 May 2009
Davies ER (2004) Machine vision: theory, algorithms, practicalities. Kaufmann, San Francisco, pp 103–130
Rodenacker K, Bengtsson E (2003) A feature set for cytometry on digitized microscopic images. Anal Cell Pathol 25:1–36
Weska JS, Charles RD, Rozenfeld A (1976) A comparative study of texture measures for terrain classification. IEEE Trans Systems, Man, and Cybernetics 6(4):269–285
Sobral JL (2005) Leather inspection based on wavelets. Pattern Recognit Image Anal 3523:682–688
Gabor D (1946) Theory of communication. J Inst Elect Eng 93(III):429–457
Kamarainen JK (2003) Feature extraction using Gabor filters. PhD thesis, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland, pp. 17–56
Bovik AC, Gopal N, Emmoth T, Restrepo A (1992) Localized measurement of emergent image frequencies by Gabor wavelets. IEEE Trans Inf Theory 38(2):691–712
Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am A 2(7):1160–1169
Tsai DM, Wu SK (2000) Automated surface inspection using Gabor filters. Int J Adv Manuf Technol 16(7):474–482
Lee TS (1996) Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18(10):959–971
Havlicek JP, Bovik AC, Maragos P (1992) Modulation models for image processing and wavelet-based image demodulation. 1992 Conference Record of Twenty-Sixth Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 805–810
Havlicek JP, Harding DS, Bovik AC (1996) Extracting essential modulated image structure. Conference Record of the Thirtieth Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 1014–1018
Havlicek JP, Bovik AC, Chen D (1999) AM-FM image modeling and Gabor analysis. In: Chen CW, Zhang Y (eds) In visual information representation, communication, and image processing. Marcel Dekker, New York, pp 343–385
Havlicek JP, Tang J, Acton ST, Antonucci R, Ouandji FN (2003) Modulation domain texture retrieval for CBIR in digital libraries. Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 1580–1584
Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842
Turner MR (1986) Texture discrimination by Gabor functions. Biol Cybern 55(2):71–82
Bovik AC, Clark M, Geisler WS (1990) Multichannel texture analysis using localized spatial filters. IEEE Trans Pattern Anal Mach Intell 12(1):55–73
Bianconi F, Fernández A (2007) Evaluation of the effects of Gabor filter parameters on texture classification. Pattern Recognit 40(12):3325–3335
Clausi DA, Jernigan ME (2000) Designing Gabor filters for optimal texture separability. Pattern Recognit 33(11):1835–1849
Kong Z, Cai Z (2007) Advances of research in fuzzy integral for classifiers’ fusion. Proceedings of the 8th ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing, Vol. 2, pp. 809–814
Mak KL, Peng P, Yiu KFC (2009) Fabric defect detection using morphological filters. Image Vis Comput 27(10):1585–1592
Amaral AL, Alves MM, Mota M, Ferreira EC (1997) Morphological characterisation of microbial aggregates by image analysis. Proceedings of the 9th Portuguese Conference on Pattern Recognition (RecPad’97), pp. 95–100
Hubel DH, Wiesel TN (1965) Receptive fields and functional architecture in two nonstriate visual areas visual areas 18 and 19 of the cat. J Neurophysiol 28:229–289
Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructures of cognition, Vol. 1. MIT, Cambridge, pp 62–318
Berchtold S, Keim DA, Kriegel HP (1996) The X-tree: an index structure for high-dimensional data. Proceedings of 22nd International Conference on Very Large Data Bases, pp. 28–39.
Berg R (2005) Sensitivity and specificity. Clin Med Res 3:56
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Automated visual inspection system installed by CARTIF in a real cutting steel factory (MPG 12108 kb)
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Medina, R., Gayubo, F., González-Rodrigo, L.M. et al. Automated visual classification of frequent defects in flat steel coils. Int J Adv Manuf Technol 57, 1087–1097 (2011). https://doi.org/10.1007/s00170-011-3352-0
- Feature extraction
- Machine vision
- Image processing
- Image classification