Detection and Classification of Surface Defects of Cold Rolling Mill Steel Using Morphology and Neural Network

Yazdchi, Mohammadreza; Mahyari, Arash Golibagh; Nazeri, Aria

doi:10.1109/cimca.2008.130

Cited by 35 publications

(42 citation statements)

References 6 publications

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“…In approaches based upon texture analysis, the main purpose is to provide a criterion for detection of image texture properties, such as softness, flatness, coarseness, etc. some studies have also been performed based on Fractal Model [6] and co-occurrence matrix in defect detection of steel sheets.…”

Section: Introductionmentioning

confidence: 99%

Application of two dimensional wavelet for defect detection in steel process

Sadeghi

saraie

Mahdeian

2011

The 2nd International Conference on Control, Instrumentation and Automation

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In any industry, quality control is an essential and inevitable part of process. Defect detection in steel plates, is one of the most important quality control steps in steel process. Image processing is a dominant technique to recognize defect in steel plates. In this paper a fast and precise solution for detection of this type of defects by using 2D wavelet is introduced. The result show considerable improvement and Precision and speed of this suggested approach is compared to the previous methods.

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Section: Introductionmentioning

confidence: 99%

Application of two dimensional wavelet for defect detection in steel process

Sadeghi

saraie

Mahdeian

2011

The 2nd International Conference on Control, Instrumentation and Automation

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“…In section 4, we explain how the Generation of Good Background and Dynamic Updating is done. In section 5, appropriate features are extracted using Entropy [6]. Section 6 describes the background subtraction Method for Defect Detection.…”

Section: Introductionmentioning

confidence: 99%

Defect detection of steel surface using entropy segmentation

Nand

Noopur

Neogi

2014

2014 Annual IEEE India Conference (INDICON)

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Today, because of high manufacturing speed in steel industry, there is a need of fast and accurate detection of steel defect for quality assurance of product. Unlike other papers on defect detection of steel surface based on entropy this paper presents a new pre-processing and processing algorithm. The method presented here overcomes the limitations of traditional segmentation method or adaptive segmentation method like Otsu's method. This paper presents a new defect detection algorithm based on entropy. As a pre-processing step illumination compensation of image has been introduced using inverse illumination to remove non-uniformity of light intensity in the image. In the second part Local entropy of image has been used to detect the region of defect. The paper also suggests the concept of dynamic updation which helps to find a good background i.e. ideal steel surface and provides an effective method to classify the defects in its initial stage into defective and non-defective image. Background subtraction method is then used to extract the defective portion of image from the entropy image by comparing the entropy of image with the entropy of background image. Histogram thresholding method has been introduced to separate the background and defective portion in the background subtracted image to get the segmented image. The method was successfully tested on three kinds of defect on steel surface i.e. water droplet, blister and scratch.Index Termssteel defect, entropy, image processing.

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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%

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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Detection and Classification of Surface Defects of Cold Rolling Mill Steel Using Morphology and Neural Network

Cited by 35 publications

References 6 publications

Application of two dimensional wavelet for defect detection in steel process

Application of two dimensional wavelet for defect detection in steel process

Defect detection of steel surface using entropy segmentation

Morphology-Based Crack Detection for Steel Slabs

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