Automated visual inspection is becoming an important field of computer vision in many industries. The real-time inspection of flat surface products is a task full of challenges in industrial aspects that requires fast and accurate algorithms for detection and localisation of defects. Structural, statistical and filter-based approaches, such as Gabor Filter Banks, Log-Gabor filter and Wavelets, have high computational complexity.
This paper introduces a fast and accurate model for inspection and localization of industrial flat surface products: Neighborhood Preserving Perceptual Fidelity Aware Mean Squared Error (NP-PAMSE). The Extreme Learning Machine (ELM) is used for classification. ELM is found to be the perfect classifier for detecting defects. The proposed model resulted in defect detection accuracy of 99.86%, with 98.16% sensitivity, and 99.90% specificity.
These results show that the proposed model outperforms many existing defect detection approaches. The discriminant power displays the efficiency of ELM in differentiation between normal and abnormal surfaces.
MadniA.M., MadniC.C. and LuceroS.D., Leveraging digital twin technology in model-based systems engineering, Systems7(1) (2019), 7.
2.
XueW., MouX., ZhangL. and FengX., Perceptual fidelity aware mean squared error. In Proceedings of the IEEE International Conference on Computer Vision, 2013, (pp. 705–712).
3.
HuG.-H., Automated defect detection in textured surfaces using optimal elliptical Gabor filters, Optik126(14) (2015), 1331–1340.
4.
MeiS., YangH. and YinZ., An unsupervised-learning-based approach for automated defect inspection on textured surfaces, IEEE Transactions on Instrumentation and Measurement67(6) (2018), 1266–1277.
5.
VujasinovicT., PribicJ., KanjerK., MilosevicN.T., TomasevicZ., Milovanovic...Z. and RadulovicM., Gray-level co-occurrence matrix texture analysis of breast tumor images in prognosis of distant metastasis risk, Microscopy and Microanalysis21(3) (2015), 646–654.
6.
GaoB., ZhangH., WooW.L., TianG.Y., BaiL. and YinA., Smooth nonnegative matrix factorization for defect detection using microwave nondestructive testing and evaluation, IEEE Transactions on Instrumentation and Measurement63(4) (2013), 923–934.
7.
JiH., YangX., LingH. and XuY., Wavelet domain multifractal analysis for static and dynamic texture classification, IEEE Transactions on Image Processing22(1) (2012), 286–299.
8.
ZhuB., LiuJ., PanR., GaoW. and LiuJ., Seam detection of inhomogeneously textured fabrics based on wavelet transform, Textile Research Journal85(13) (2015), 1381–1393.
9.
ZuñigaA.G., FlorindoJ.B. and BrunoO.M., Gabor wavelets combined with volumetric fractal dimension applied to texture analysis, Pattern Recognition Letters36 (2014), 135–143.
10.
LiY. and ZhangC., Automated vision system for fabric defect inspection using Gabor filters and PCNN, SpringerPlus5(1) (2016), 765.
11.
SchneiderD. and MerhofD., Blind weave detection for woven fabrics, Pattern Analysis and Applications18(3) (2015), 725–737.
12.
HuG.-H., WangQ.-H. and ZhangG.-H., Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage, Applied optics54(10) (2015), 2963–2980.
13.
LiP., ZhangH., JingJ., LiR. and ZhaoJ., Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method, The Journal of The Textile Institute106(6) (2015), 587–592.
14.
ÇelikH., DülgerL. and TopalbekiroğluM., Development of a machine vision system: real-time fabric defect detection and classification with neural networks, The Journal of The Textile Institute105(6) (2014), 575–585.
15.
LiY., AiJ. and SunC., Online fabric defect inspection using smart visual sensors, Sensors13(4) (2013), 4659–4673.
16.
ÇelikH.I., TopalbekitogluM. and DülgerL., Real-time denim fabric inspection using image analysis, Fibres & Textiles in Eastern Europe, 2015.
17.
BissiL., BaruffaG., PlacidiP., RicciE., ScorzoniA. and ValigiP., Automated defect detection in uniform and structured fabrics using Gabor filters and PCA, Journal of Visual Communication and Image Representation24(7) (2013), 838–845.
18.
JingJ., YangP., LiP. and KangX., Supervised defect detection on textile fabrics via optimal Gabor filter, Journal of Industrial Textiles44(1) (2014), 40–57.
19.
RenR., HungT. and TanK.C., A generic deep-learning-based approach for automated surface inspection, IEEE transactions on cybernetics48(3) (2018), 929–940.
20.
WeimerD., Scholz-ReiterB. and ShpitalniM., Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection, CIRP Annals65(1) (2016), 417–420.
21.
ParkJ.K., KwonB.K., ParkJ.H. and KangD.J., Machine learning-based imaging system for surface defect inspection, International Journal of Precision Engineering and Manufacturing-Green Technology3(3) (2016), 303–310.
22.
LiY., ZhaoW. and PanJ., Deformable patterned fabric defect detection with fisher criterion-based deep learning, IEEE Transactions on Automation Science and Engineering14(2) (2017), 1256–1264.
23.
HanbayK., TaluM.F. and ÖzgüvenÖ.F., Fabric defect detection systems and methods—A systematic literature review, Optik127(24) (2016), 11960–11973.
24.
DingS., ZhaoH., ZhangY., XuX. and NieR., Extreme learning machine: algorithm, theory and applications, Artificial Intelligence Review44(1) (2015), 103–115.
25.
LiW., ChenC., SuH. and DuQ., Local binary patterns and extreme learning machine for hyperspectral imagery classification, IEEE Transactions on Geoscience and Remote Sensing53(7) (2015), 3681–3693.
26.
RaafatH.M. and TolbaA., Homoscedasticity for defect detection in homogeneous flat surface products, Textile Research Journal85(8) (2015), 850–866.
27.
SokolovaM., JapkowiczN. and SzpakowiczS., Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. in Australasian joint conference on artificial intelligence. 2006. Springer.
TolbaA., Neighborhood-preserving cross correlation for automated visual inspection of fine-structured textile fabrics, Textile Research Journal81(19) (2011), 2033–2042.
30.
TolbaA., Fast defect detection in homogeneous flat surface products, Expert Systems with Applications38(10) (2011), 12339–12347.
31.
ZhangD., GaoG. and Li.C. Fabric defect detection algorithm based on Gabor filter and low-rank decomposition. in Eighth International Conference on Digital Image Processing (ICDIP 2016). 2016. International Society for Optics and Photonics.
32.
LiuL., ZhangJ., FuX., LiuL. and HuangQ., Unsupervised segmentation and elm for fabric defect image classification, Multimedia Tools and Applications78(9) (2019), 12421–12449.
33.
HuG.-H., ZhangG.-H. and WangQ.-H., Automated defect detection in textured materials using wavelet-domain hidden Markov models, Optical Engineering53(9) (2014), 093107.
34.
EssaE., HossainM.S., TolbaA.S., RaafatH.M., ElmogyS. and MuahmmadG., Toward cognitive support for automated defect detection, Neural Computing and Applications, 2019, 1–9.
35.
TongL., WongW.K. and KwongC., Fabric defect detection for apparel industry: a nonlocal sparse representation approach, IEEE access5 (2017), 5947–5964.
