Surface defect classification and detection on extruded aluminum profiles using convolutional neural networks

International Journal of Material Forming - Tập 13 Số 4 - Trang 591-603 - 2020
Felix M. Neuhauser1, Gregor Bachmann1, Pavel Hora1
1Institute of Virtual Manufacturing, Department of Mechanical and Process Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland

Tóm tắt

Từ khóa


Tài liệu tham khảo

Qamar SZ, Arif AFM, Sheikh AK (2004) Analysis of product defects in a typical aluminum extrusion facility. Mater Manuf Process 19(3):391–405

Chondronasios A, Popov I, Jordanov I (2016) Feature selection for surface defect classification of extruded aluminum profiles. Int J Adv Manuf Technol 83(1–4):33–41

Gonzalez-Adrados JR, Pereira H (1996) Classification of defects in cork planks using image analysis. Wood Sci Technol 30(3):207–215

Bishop CM (2006) Pattern recognition and machine learning. Information science and statistics. Springer

Lopes F, Pereira H, Natale FGB, De Tintrup F, Giusto DD, Vernazza G (1995) Cork pores and defects detection by morphological image analysis. Wood Science and Technology

Georgieva A, Jordanov I (2007) Image processing techniques for cork tiles classification. In: 2007 IEEE international conference on signal processing and communications, pp 576–579

Di L, Liang L-Q, Zhang W-J (2014) Defect inspection and extraction of the mobile phone cover glass based on the principal component analysis. Int J Adv Manuf Technol 73(9–12):1605–1614

Shlens J (2005) A tutorial on principal component analysis. arXiv: 1404.1100

Engelhardt M, Behne D, Grittner N, Neumann A, Reimche W, Klose C (2015) Non-destructive testing of longitudinal and charge weld seams in extruded aluminum and magnesium profiles, vol 2

Garbacz P, Giesko T, Mazurkiewicz A (2015) Inspection method of aluminium extrusion process. Arch Civil Mech Eng 15(3):631–638

Zhang X-w, Ding Y-q, Lv Y-y, Shi A-y, Liang R-y (2011) A vision inspection system for the surface defects of strongly reflected metal based on multi-class svm. Expert Syst Appl 38(5):5930–5939

Park J-K, Kwon B-K, Park J-H, Kang D-J (2016) Machine learning-based imaging system for surface defect inspection. Int J Precis Eng Manuf Green Technol 3(3):303–310

Ciora RA, Simion CM (2014) Industrial applications of image processing. Acta Universitatis Cibiniensis–Technical Series 64(1):17–21

Tzutalin (2015) Labelimg. https://github.com/tzutalin/labelImg . Git code

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778

Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1–9

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) (* = equal contribution). Imagenet large scale visual recognition challenge IJCV. http://www.image-net.org/challenges/LSVRC/

Demant C, Streicher-Abel B, Garnica C (2013) Industrial image processing: visual quality control in manufacturing, 2 edn. Springer

Yao Y, Rosasco L, Caponnetto A (2007) On early stopping in gradient descent learning. Constr Approx 26(2):289–315

Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., pp 1097–1105

Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, Lecun Y (2014) Overfeat: integrated recognition, localization and detection using convolutional networks. In: International conference on learning representations (ICLR2014), CBLS

Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: ECCV

Diederik PK, Ba J (2014) Adam: a method for stochastic optimization. arXiv: 1412.6980

Ren S, He K, Girshick RB, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137–1149

Girshick RB, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on computer vision and pattern recognition, pp 580–587

Girshick RB (2015) Fast R-CNN. In: 2015 IEEE international conference on computer vision (ICCV), pp 1440–1448

Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S, Murphy K (2016) Speed/accuracy trade-offs for modern convolutional object detectors. arXiv: 1611.10012

Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv: 1609.04747