Defect detection for corner cracks in steel billets using a wavelet reconstruction method

Jeon, Yong-Ju; Choi, Doo-chul; Lee, Sang Jun; Yun, Jong Pil; Kim, Sang Woo

doi:10.1364/josaa.31.000227

Cited by 61 publications

(38 citation statements)

References 35 publications

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“…Gray-scale intensity imaging is commonly used, in combination with various different signal processing techniques such as wavelet transforms [12], [13], Gabor filters [4] and image morphology [4], [5], [14]. Use of gray-scale intensity images has its limitations though.…”

Section: B Related Researchmentioning

confidence: 99%

“…Variations in lightning conditions, giving rise to potential pseudo-defects, are a problem. In particular, light reflection from scale regions may vary substantially, making the gray level in intensity images highly unpredictable which may give rise to psuedo-defects [13]. Other parameters, such as steel type, may effect properties in gray-scale images as well [5].…”

Section: B Related Researchmentioning

confidence: 99%

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Morphology-Based Crack Detection for Steel Slabs

Landström

Thurley

2012

IEEE J. Sel. Top. Signal Process.

110

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Abstract-Continuous casting is a highly efficient process used to produce most of the world steel production tonnage, but can cause cracks in the semi-finished steel product output. These cracks may cause problems further down the production chain, and detecting them early in the process would avoid unnecessary and costly processing of the defective goods. In order for a crack detection system to be accepted in industry, however, false detection of cracks in non-defective goods must be avoided. This is further complicated by the presence of scales; a brittle, often cracked, top layer originating from the casting process.We present an approach for an automated on-line crack detection system, based on 3D profile data of steel slab surfaces, utilizing morphological image processing and statistical classification by logistic regression. The initial segmentation successfully extracts 80% of the crack length present in the data, while discarding most potential pseudo-defects (non-defect surface features similar to defects). The subsequent statistical classification individually has a crack detection accuracy of over 80% (with respect to total segmented crack length), while discarding all remaining manually identified pseudo-defects. Taking more ambiguous regions into account gives a worst-case false classification of 131 mm within the 30 600 mm long sequence of 150 mm wide regions used as validation data. The combined system successfully identifies over 70% of the manually identified (unambiguous) crack length, while missing only a few crack regions containing short crack segments.The results provide proof-of-concept for a fully automated crack detection system based on the presented method.

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Section: B Related Researchmentioning

confidence: 99%

Section: B Related Researchmentioning

confidence: 99%

Morphology-Based Crack Detection for Steel Slabs

Landström

Thurley

2012

IEEE J. Sel. Top. Signal Process.

110

View full text Add to dashboard Cite

show abstract

“…Image segmentation is an indispensable step for most popular defect detection methods, including statistical methods, 2-9 model-based methods, [10][11][12] and frequency spectral methods. [13][14][15][16][17][18][19][20][21] In all of these methods, the determination and location of defects depends on image segmentation. That is, almost all surface defect detection methods must segment each image to be inspected before determining whether the product is defective or not.…”

Section: Introductionmentioning

confidence: 99%

Rapid surface defect detection based on singular value decomposition using steel strips as an example

2018

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On the radiated EMI current extraction of dc transmission line based on corona current statistical measurements AIP Advances 8, 055001 (2018) For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection.

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“…6) In the steel manufacturing industry, various vision-based defect inspection systems have been introduced. 7,8) An automatic defect detection system using Gabor filters optimized by a univariate dynamic encoding algorithm for searches has been developed for detecting cracks in raw steel blocks. 9) A real-time vision-based defect inspection system for coiled steel bars for high-speed applications has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pinholes in Steel Slabs Using Gabor Filter Combination and Morphological Features

et al. 2017

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In this paper, an algorithm for detecting pinholes was proposed. Pinholes are very tiny holes generated in the surfaces of scarfed slabs. To detect various sizes of pinholes, the Gabor filter combination was applied. A new image segmentation method was proposed in pre-processing. To reduce the number of pseudo-defects, dual thresholding method was used. We have increased the performance of classification by adding new morphological features to general texture based features. To evaluate the performance of proposed algorithm, an images obtained in real production line were used.

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Defect detection for corner cracks in steel billets using a wavelet reconstruction method

Cited by 61 publications

References 35 publications

Morphology-Based Crack Detection for Steel Slabs

Morphology-Based Crack Detection for Steel Slabs

Rapid surface defect detection based on singular value decomposition using steel strips as an example

Detection of Pinholes in Steel Slabs Using Gabor Filter Combination and Morphological Features

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