Automatic Defect Detection on Hot-Rolled Flat Steel Products

Ghorai, Santanu; Mukherjee, Anirban; Gangadaran, M.; Dutta, Pranab

doi:10.1109/tim.2012.2218677

Cited by 266 publications

(121 citation statements)

References 16 publications

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“…How to extract a set of better feature representations and design the appropriate classifier for surface defects has been a hot research topic for many years. [1][2][3][4][5][6][7][8] A lot of methods about feature extraction and classification for image have been developed [9][10][11][12][13][14][15][16][17] , M. X. Chu et al 16 extracted features of geometry, gray, projection, texture and frequency-domain of defect in steel, then an enhanced twin support vector machine was adopted to realize the classification. A. Cord et al 12 proposed a classification method of statistical learning based on a textural feature for defect of metallic surface.…”

Section: Introductionmentioning

confidence: 99%

“…A. Cord et al 12 proposed a classification method of statistical learning based on a textural feature for defect of metallic surface. S. Ghorai 7 derived a set of good-quality defect descriptors from wavelet feature set and applied support vector machine to the classification and detection of the defects. These traditional methods usually use handcrafted features, such as geometrical shape 11,13,15,16 , grayscale 1,13,16 , texture 3,[10][11][12] , local binary pattern 8 , wavelet transform [4][5][6][7]9 or their combinations 2,11,16 , followed by a trainable classifier, such as artificial neural networks 9,11,14 , support vector machine [6][7][8]13,15 and so on.…”

Section: Introductionmentioning

confidence: 99%

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Classification of surface defects on steel sheet using convolutional neural networks

Zhou¹,

Chen²,

Zhang³

et al. 2017

Mater. Tehnol.

107

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A convolutional neural network (CNN) is proposed to learn multiple useful feature representations for a classification from low level (raw pixels) to high level (object). Convolutional kernels are initialized by the learned filter kernels that come from sparse auto-encoders. Unlike some traditional methods, which divide the feature abstracting and classifier training into two separated processes, a discriminative feature vector and a single multi-class classifier of softmax regression are learned simultaneously during the training process. Based on the learned high-quality feature representation, the classification can be efficiently performed. A real-world case of surface defects on steel sheet, which evaluates the classification performance of the proposed method, is depicted in detail. The experimental results indicate that the proposed method is quite simple, effective and robustness for the classification of surface defects on hot-rolled steel sheet. Keywords: convolutional neural networks, classification, surface defects, steel sheet, convolutional kernels, sparse auto-encoder Konvolucijska nevronska mre`a (CNN) je predlagana za u~enje {tevilnih koristnih predstavitev pri klasifikaciji od nizkega nivoja (grobe slikovne pike) do visokega nivoja (predmet). Konvolucijska jedra so inicializirana z nau~enimi filtrirnimi jedri, ki izhajajo iz redkih samoenkoderjev. Razli~no od nekaterih klasi~nih metod, ki delijo funkcijo abstrakcije in trening klasifikacije v dva lo~ena procesa, se vektor nediskriminativne funkcije v enostavnem ve~razrednem klasifikatorju regresije softmax, u~i hkrati med procesom treninga. Na osnovi nau~ene predstavitve z visoko kvalitetno funkcijo, se lahko klasifikacija u~inkovito izvede. Primer iz resni~nega sveta povr{inske napake na jekleni plo~evini, ki ocenjujejo zmogljivost klasifikacije je prikazan v podrobnostih. Rezultati eksperimentov ka`ejo, da je predlagana metoda razmeroma preprosta, u~inkovita in robustna pri klasifikaciji povr{inskih napak na vro~e valjani jekleni plo~evini.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of surface defects on steel sheet using convolutional neural networks

Zhou¹,

Chen²,

Zhang³

et al. 2017

Mater. Tehnol.

107

View full text Add to dashboard Cite

show abstract

“…In [2,3], the defects were classified by using K-nearest neighbor (KNN) methods with a co-occurrence matrix. Santanu Ghorai et al [4] described an automated visual inspection system with discrete wavelet transform (DWT) features and a support vector machine (SVM). Wu et al [5] described an algorithm with an undecimated wavelet transform (UWT) and a mathematical morphology to detect geometric defects that achieved a 90.23% accuracy that is difficult to achieve in real industrial application in 2008.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary classifier for steel surface defects with small sample set

Xiao

Jiang

et al. 2017

J Image Video Proc.

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Nowadays, surface defect detection systems for steel strip have replaced traditional artificial inspection systems, and automatic defect detection systems offer good performance when the sample set is large and the model is stable. However, the trained model does work well when a new production line is initiated with different equipment, processes, or detection devices. These variables make just tiny changes to the real-world model but have a significant impact on the classification result. To overcome these problems, we propose an evolutionary classifier with a Bayes kernel (BYEC) that can be adjusted with a small sample set to better adapt the model for a new production line. First, abundant features were introduced to cover detailed information about the defects. Second, we constructed a series of support vector machines (SVMs) with a random subspace of the features. Then, a Bayes classifier was trained as an evolutionary kernel fused with the results from the sub-SVM to form an integrated classifier. Finally, we proposed a method to adjust the Bayes evolutionary kernel with a small sample set. We compared the performance of this method to various algorithms; experimental results demonstrate that the proposed method can be adjusted with a small sample set to fit the changed model. Experimental evaluations were conducted to demonstrate the robustness, low requirement for samples, and adaptiveness of the proposed method.

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“…Most of existing works have focused on these two aspects to enhance the classification performance over the past few years. [9][10][11][12][13][14][15][16][17][18][19][20] A set of features are studied, including grayscale features, 9,10) geometric features, 9,10) shape features, [9][10][11] texture features, 10,12) Gabor filter output features, 13) Fourier spectral, 14) wavelet transform, [14][15][16][17] local binary pattern (LBP), 18,19) shearlet transform; 20) and the classifier of artificial neural network (ANN) 12,13,15) and support vector machine (SVM) 9,10,14,[17][18][19][20] is discussed. However, these approaches, which treat feature extraction and classifier training as two separating steps, are difficult to control the interaction of two steps.…”

Section: Introductionmentioning

confidence: 99%

Learning a Class-specific and Shared Dictionary for Classifying Surface Defects of Steel Sheet

et al. 2017

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An approach to a class-specific and shared dictionary learning (CDSDL) for sparse representation is proposed to classify surface defects of steel sheet. The proposed CDSDL algorithm is modelled as a unified objective function, covering reconstructive error, sparse and discriminative promotion constraints. With the high-quality dictionary, the compact, reconstructive and discriminative feature representation of an image can be extracted. Then the classification can be efficiently performed by discriminative information obtained from the reconstructive error or the sparse vector. Based on a dataset of surface images captured from a practical steel production line, the CDSDL algorithm is carried out to verify its effectiveness. Experimental results indicate that the CDSDL algorithm is more effective in classifying surface defects of steel sheet than other algorithms.KEY WORDS: surface defects of steel sheet; image classification; sparse representation; discriminative dictionary learning.

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Automatic Defect Detection on Hot-Rolled Flat Steel Products

Cited by 266 publications

References 16 publications

Classification of surface defects on steel sheet using convolutional neural networks

Classification of surface defects on steel sheet using convolutional neural networks

An evolutionary classifier for steel surface defects with small sample set

Learning a Class-specific and Shared Dictionary for Classifying Surface Defects of Steel Sheet

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