Strip Steel Surface Defects Recognition Based on SOCP Optimized Multiple Kernel RVM

Hou, Jun; Xia, Kewen; Yang, Fan; Zu, Baokai

doi:10.1155/2018/9298017

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Zhang et al [125] and Wang et al [51] improved the random forest algorithm to execute defect classification on steel surfaces. Hou et al [126] proposed a second-order cone programming (SOCP) optimized multiple kernel RVM to recognize strip steel surface defects, which showed better performance than both the traditional RVM and the original SVM. The classification methods based on supervised learning can excute sufficient training and learning on the images to obtain the most effective representation.…”

Section: ) Other Classifiers and Brief Summarymentioning

confidence: 99%

Automated Visual Defect Classification for Flat Steel Surface: A Survey

Luo

Fang

et al. 2020

IEEE Trans. Instrum. Meas.

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For a typical surface automated visual inspection (AVI) instrument of planar materials, defect classification is an indispensable part after defect detection, which acts as a crucial precondition for achieving the on-line quality inspection of end products. In the industrial environment of manufacturing flat steels, this task is awfully difficult due to diverse defect appearances, ambiguous intraclass and interclass distances. This paper attempts to present a focused but systematic review of the traditional and emerging automated computer-vision-based defect classification methods by investigating approximately 140 studies on three specific flat steel products of con-casting slabs, hot-rolled steel strips and cold-rolled steel strips. According to the natural image processing procedure of defect recognition, the diverse approaches are grouped into five successive parts: image acquisition, image preprocessing, feature extraction, feature selection and defect classifier. Recent literature has been reviewed from an industrial goal-oriented perspective to provide some guidelines for future studies, as well as to recommend suitable methods for boosting the surface quality inspection level of AVI instruments.

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Section: ) Other Classifiers and Brief Summarymentioning

confidence: 99%

Automated Visual Defect Classification for Flat Steel Surface: A Survey

Luo

Fang

et al. 2020

IEEE Trans. Instrum. Meas.

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“…In the last decades, machine learning techniques have started to be employed for solving IP problems [11][12][13][14][15][16]. Nevertheless, it is not easy to find learning approaches employed for low-level processing tasks, for instance, IP tasks lower than contour detection [17,18].…”

Section: Introductionmentioning

confidence: 99%

Postprocessing of Edge Detection Algorithms with Machine Learning Techniques

Flores-Vidal

Castro

Gómez

2022

Mathematical Problems in Engineering

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In this paper, machine learning (ML) techniques are applied at an early stage of Image Processing (IP). The learning procedures are usually applied from at least the image segmentation level, whereas, in this paper, this is done from a lower processing level: the edge detection level (ED). The main objective is to solve the edge detection problem through ML techniques. The proposed methodology is based on a classification of edges made pixel by pixel, but the predictors employed for the ML task include information about the pixel neighborhood and structures of connected pixels called edge segments. The Sobel operator is employed as input. Making use of 50 images that belong to the Berkeley Computer Vision data set, the average performance of the validation sets when employing our Neural Networks method reached an F-measure significatively higher than with the Sobel operator. The experiment results show that our post-processing technique is a promising new approach for ED.

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A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

Bártová

Bína

Váchová

2022

Prod.

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Paper aims: This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations.Originality: Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing.Research method: In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004). Main findings:The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used. Implications for theory and practice:This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose.

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Strip Steel Surface Defects Recognition Based on SOCP Optimized Multiple Kernel RVM

Cited by 3 publications

References 10 publications

Automated Visual Defect Classification for Flat Steel Surface: A Survey

Automated Visual Defect Classification for Flat Steel Surface: A Survey

Postprocessing of Edge Detection Algorithms with Machine Learning Techniques

A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry

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