Research on Surface Quality Evaluation System of Steel Strip Based on Computer Vision

Zhao, Jiuliang; Liu, Weiwei; Yan, Feng; Li, Jun; Zhang, Yao; Yan, Yunhui

doi:10.1109/iita.2009.45

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…There is also another kind of the proposed methods which attempts to solve this problem by employing machine learning and artificial intelligence techniques alone, or sometimes by combining them with previous methods. 22 Jiuliang et al 23 proposed a fuzzy expert system to assess the steel strips surface quality which consists of six components. A monitoring system was proposed by Zheng et al 24 for detecting the metal surface defections, which in particular could detect the cracks and existing gaps on aluminum surfaces.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces

Torkzadeh

Toosizadeh

2019

Measurement and Control

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In this paper, an automated inspection system is proposed for detecting the location and measuring the size of existing dipping or buckling on the sandwich panel surface using an RGB camera and an inexpensive linear laser. The proposed method, by observing the radiated laser beams on the sandwich panel surface, can localize and calculate the level of dipping and buckling with acceptable accuracy while being robust to vibrations of moving sandwich panel on the production line conveyor. After a complete processing of the panel by the system, a three-dimensional (surface plot) or two-dimensional (heat map) output is produced to assist the production line supervisor to easily inspect the surface quality of the sandwich panels. Our experimental results show that the proposed system can detect and measure the surface defects including dipping and buckling with a high accuracy and performance.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces

Torkzadeh

Toosizadeh

2019

Measurement and Control

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“…Many image processing algorithms have been used in the past for defect detection: for instance, in [7] four types of steel surface defects (hole, scratch, coil break and rust) are detected through several image processing algorithms and the best performing method has been selected; another image processing algorithm to detect surface defects on steel surface is discussed in [8]. Other methods for the recognition of defects on steel surface images include mathematical morphology, like in [9], or several algorithms of artificial intelligence, such as neural networks [9] [10] [11] and support vector machines [12].…”

Section: Introductionmentioning

confidence: 99%

A fuzzy inference system applied to defect detection in flat steel production

Borselli¹,

Colla²,

Vannucci³

et al. 2010

International Conference on Fuzzy Systems

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Recently in many industrial fields the exploitation of vision systems for quality control had a considerable increase, which is mainly due to the technological progress experienced by such systems, that, with respect to the past, made their performance more appealing and more reliable while the associated costs are decreased. The advantages of these kind of systems in terms of savings in human resources and improved quality monitoring have become far more evident, by encouraging their adoption in a wide variety of production cycles.The present paper deals with the elaboration and information extraction from images, that represent portions of the surface of flat steel products, and describes an algorithm for defect detection and classification. The overall classification procedure is composed of a preliminary part that is mostly related to image processing and analysis, which aims at pointing out the defect (independently on the class it belongs to), as well as to the extraction of relevant features of the detected defect; the second part exploits a fuzzy inference system in order to analyze the type of defect and solves a classification problem that presently can be addressed only with the support of a human operator. Fuzzy inference systems are suitable to this application because they are able to mimic and reproduce the human reasoning.

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“…Mientras la banda pasa entre las cámaras de video del sistema, éstas toman imágenes que posteriormente se procesan con diferentes técnicas de visión por computador [2] [3]. En este artículo se optimiza el conjunto de parámetros del método de detección que maximizan una métrica calculada mediante la comparación de una detección manual de un experto y la detección realizada por el método.…”

Section: Introduccionunclassified

A Parallel Genetic Algorithm for Configuring Defect Detection Methods

Calle

Bulnes

García

et al. 2015

IEEE Latin Am. Trans.

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The detection of defects in steel strips is a very important task which can improve the performance of factories by giving the possibility of early and real-time detection of defects. Defect detection methods have such a large amount of parameters that makes finding the best configuration a complex task. The search space of the value of these parameters is pretty large also, so it is necessary to use a search algorithm in order to reduce the computing time. In this article a genetic algorithm is developed for solving this search problem. The genetic algorithm looks for an optimal or sub-optimal solution without examining the whole search space. In addition, the computing time can be reduced by running the algorithm on a grid of computers. The genetic algorithm designed allows a near-optimal configuration of defect detection methods in a short time. , respectively. In recent years, he has been working on real-time imaging research projects in computer engineering. His research interests include real-time imaging systems and range measurement techniques.

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Research on Surface Quality Evaluation System of Steel Strip Based on Computer Vision

Cited by 5 publications

References 13 publications

Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces

Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces

A fuzzy inference system applied to defect detection in flat steel production

A Parallel Genetic Algorithm for Configuring Defect Detection Methods

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