Abstract
This research investigates detection and classification of two types of the surface defects in extruded aluminium profiles; blisters and scratches. An experimental system is used to capture images and appropriate statistical features from a novel technique based on gradient-only co-occurrence matrices (GOCM) are proposed to detect and classify three distinct classes; non-defective, blisters and scratches. The developed methodology makes use of the Sobel edge detector to obtain the gradient magnitude of the image (GOCM). A comparison is made between the statistical features extracted from the original image (GLCM) and those extracted from the gradient magnitude (GOCM). This paper describes in detail every step of the image processing with example pictures illustrating the methodology. The features extracted from the image processing are classified by a two-layer feed-forward artificial neural network. The artificial neural network training is tested using different combinations of statistical features with different topologies. Features are compared individually and grouped. Results are discussed, achieving up to 98.6 % total testing accuracy.
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References
Arif A, Sheikh A, Qamar S, Raza M, Al-Fuhaid K (2002) Product defects in aluminum extrusion and their impact on operational cost. In: Proceedings of the 6th Saudi Engineering Conference, pp 137–154
Caleb-Solly P, Smith JE (2007) Adaptive surface inspection via interactive evolution. Image Vis Comput 25(7):1058–1072
Davies ER (2012) Computer and machine vision: theory, algorithms, practicalities, Academic Press
Garbacz P, Giesko T (2013) Inspection of aluminium extrusion using infrared thermography
Georgieva A, Jordanov I (2009) Intelligent visual recognition and classification of cork tiles with neural networks. Neural Networks, IEEE Transactions on 20(4):675–685
Gonzalez R C, Woods RE (2002) Digital image processing. Prentice hall Upper Saddle River, NJ
Haralick RM (1979) Statistical and structural approaches to texture. Proceedings of the IEEE 67(5):786–804
Haralick R M, Shanmugam K, Dinstein I H (1973) Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on (6):610–621
Jia H, Murphey Y L, Shi J, Chang TS (2004) An intelligent real-time vision system for surface defect detection. In: Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, IEEE, vol 3, pp 239–242
Kumar A (2008) Computer-vision-based fabric defect detection: a survey. Industrial Electronics. IEEE Transactions on 55(1):348–363
Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533. doi:10.1016/S0893-6080(05)80056-5
Ng HF (2006) Automatic thresholding for defect detection. Pattern recognition letters 27(14):1644–1649
Oliveira V, Knapic S, Pereira H (2013) Classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines, Food and Bioproducts Processing
Ortiz-Jaramillo B, Orjuela-Vargas SA, Van-Langenhove L, Castellanos-Domnguez CG, Philips W (2014) Reviewing, selecting and evaluating features in distinguishing fine changes of global texture. Pattern Anal Applic 17(1):1–15
Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285-296):23–27
Pernkopf F, O’Leary P (2003) Image acquisition techniques for automatic visual inspection of metallic surfaces. NDT & E International 36(8):609–617
Perona P, Scale-space Malik J, diffusion edge detection using anisotropic (1990) Pattern Analysis and Machine Intelligence. IEEE Transactions on 12(7):629–639
Petrov N, Georgieva A, Jordanov I (2013) Self-organizing maps for texture classification. Neural Comput & Applic 22(7–8):1499–1508
Qamar S, Arif A, Sheikh A (2004) Analysis of product defects in a typical aluminum extrusion facility. Mater Manuf Process 19(3):391–405
Saha PK (2000) Aluminum extrusion technology. ASM International
Shafeek H, Gadelmawla E, Abdel-Shafy A, Elewa I (2004) Assessment of welding defects for gas pipeline radiographs using computer vision. NDT & E International 37(4):291–299
Shanbhag PM, Deshmukh M, Suralkar S (2012) Overview: methods of automatic fabric defect detection. Global Journal of Engineering, Design & Technology 1(2):42–46
Sheppard T (1999) Extrusion of aluminium alloys. Springer
Sobel I (1990) An isotropic 3 3 image gradient operator. Machine Vision for three-demensional Sciences
Tsai DM, Chao SM (2005) An anisotropic diffusion-based defect detection for sputtered surfaces with inhomogeneous textures. Image Vis Comput 23(3):325–338
Tsai DM, Chang CC, Chao SM (2010) Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion. Image Vis Comput 28(3):491–501
Wang-Cheung Lam S (1996) Texture feature extraction using gray level gradient based co-occurence matrices. In: Systems, Man, and Cybernetics, 1996., IEEE International Conference on, IEEE, vol 1, pp 267–271
Xue-Wu Z, Yan-Qiong D, Yan-Yun L, Ai-Ye S, Rui-Yu L (2011) A vision inspection system for the surface defects of strongly reflected metal based on multi-class svm. Expert Systems with Applications 38 (5):5930–5939
Zheng H, Kong LX, Nahavandi S (2002) Automatic inspection of metallic surface defects using genetic algorithms. J Mater Process Technol 125:427–433
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Chondronasios, A., Popov, I. & Jordanov, I. Feature selection for surface defect classification of extruded aluminum profiles. Int J Adv Manuf Technol 83, 33–41 (2016). https://doi.org/10.1007/s00170-015-7514-3
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DOI: https://doi.org/10.1007/s00170-015-7514-3