Automated visual inspection of rolled metal surfaces

Piironen, Timo; Silvén, Olli; Laitinen, Toni; Strömmer, Esko

doi:10.1007/bf01211850

Cited by 37 publications

(17 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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. They mainly include three stages: 1) Locating the position of surface defects (Detection).…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

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.

show abstract

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

“…This condition is shown in Figure 2.26 for a pit-type imperfection. Effective inspection of automotive paints and finishes requires a lighting environment that produces strong luminance edges which can be seen in reflection from the surface of an automobile (52)(53)(54). Paint imperfections such as embedded dirt, runs, sags, and dents reflect light from many different angles.…”

Section: Contrast Sensitivitymentioning

confidence: 99%

AISI/DOE Technology Roadmap Program: Improved Surface Quality of Exposed Automotive Sheet Steels

Speer¹,

Matlock²,

Meyers³

et al. 2002

View full text Add to dashboard Cite

“…A two-level tree classifier using non-parametric linear classifier at each node is used for classification of defects. One of the work employing modern facilities is presented in [13], in which a proto-type of an automated visual on-line metal strip inspection system, employing CCD camera and capable of both detecting and classifying the surface defects of copper alloy strip was outlined.…”

Section: Introductionmentioning

confidence: 99%

Metal surface defect detection using iterative thresholding technique

Senthikumar¹,

Palanisamy

Jain

2014

Second International Conference on Current Trends in Engineering and Technology - ICCTET 2014

View full text Add to dashboard Cite

Recently, surface defects detection in metals plays a significant role in computer vision applications. An efficient and accurate defect detection approach is implemented in this paper. The defect detection on metal surface is achieved by iterative thresholding technique on metal surface images. The defect region such as crack and shrinkage of the metal surface image is detected by binarization using iterative thresholding technique. The experimental results are carried out by using real time metal surface images and satisfactory performance is achieved by the proposed defect detection technique.

show abstract

Automated visual inspection of rolled metal surfaces

Cited by 37 publications

References 6 publications

Classification of surface defects on steel sheet using convolutional neural networks

Classification of surface defects on steel sheet using convolutional neural networks

AISI/DOE Technology Roadmap Program: Improved Surface Quality of Exposed Automotive Sheet Steels

Metal surface defect detection using iterative thresholding technique

Contact Info

Product

Resources

About