Steel Strip Surface Defect Identification using Multiresolution Binarized Image Features

Mentouri, Zoheir; Moussaoui, Abdelkrim; Boudjehem, Djalil; Doghmane, Hakim

doi:10.1007/s11668-020-01012-7

Cited by 23 publications

(12 citation statements)

References 17 publications

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“…Research conducted by the authors Mentouri et al [ 6 ] employs the method of Binarized Statistical Image Features, which was used in biometrics until now. A higher average rate of recognition has been achieved in this study, namely of six types of defects occurring in the hot-rolling process.…”

Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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This paper describes the enhancement of the existing predictive system of quality management in the processes of metallurgic manufacturing. Specifically, it addresses steel-strip manufacturing. The main quality management innovation is the transition from the current methodological process of a single-step defect evaluation to a two-step evaluation. A two-step defect check of the strip’s surface involves checking for defects during the hot-rolling process first, and double-checking it during the process of pickling. These defects are detected in a well-established process of camera imaging in the production process. The recorded image is then processed mathematically to find the degree of defect correlation in those processes. The two-step evaluation enables a more detailed focus on a particular defect and its position on the strip. Decisions concerning further processing are based on defect evaluation, for instance, whether a rework is necessary to maximize the product utilization and minimize the eventual negative impact of the defect on production equipment. A crucial aspect is also the reduced probability of failures in the manufacturing process.

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Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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“…Mentouri et al. [164] used the KNN combining with binarized statistical feature extractor to efficiently recognize strip surface defects in the hot rolling process.…”

Section: Image Processing Algorithmmentioning

confidence: 99%

Review of surface defect detection of steel products based on machine vision

Tang

Chen

Sun

et al. 2022

IET Image Processing

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Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision‐based surface defect inspection for steel products including hardware system, automated vision‐based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre‐processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

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“…Creating optimal lighting conditions is a complex technical task, which is part of the general problem consisting in improving the accuracy of systems for identifying technological defects in rolled metal. In addition to theoretical calculations, the creation of the optimal lighting conditions requires direct experiments [13,14]. Front lighting is commonly used in practice, which provides for uniform light distribution and a small number of shadows or illuminated areas [1,2].…”

Section: Introductionmentioning

confidence: 99%

Influence of Uneven Lighting on Quantitative Indicators of Surface Defects

et al. 2022

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The impact of the illumination level on the quantitative indicators of mechanical damage of the rolled strip is investigated. To do so, a physical model experiment was conducted in the laboratory. The obtained images of defects at light levels in the range of 2–800 lx were recognized by a neural network model based on the U-net architecture with a decoder based on ResNet152. Two levels of illumination were identified, at which the total area of recognized defects increased: 50 lx and 300 lx. A quantitative assessment of the overall accuracy of defect recognition was conducted on the basis of comparison with data from images marked by an expert. The best recognition result (with Dice similarity coefficient ) was obtained for the illumination of 300 lx. At lower light levels (less than 200 lx), some of the damage remained unrecognized. At high light levels (higher than 500 lx), a decrease in DSC was observed, mainly due to the fact that the surface objects are better visible and the recognized fragments become wider. In addition, more false-positives fragments were recognized. The obtained results are valuable for further adjustment of industrial systems for diagnosing technological defects on rolled metal strips.

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Steel Strip Surface Defect Identification using Multiresolution Binarized Image Features

Cited by 23 publications

References 17 publications

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Review of surface defect detection of steel products based on machine vision

Influence of Uneven Lighting on Quantitative Indicators of Surface Defects

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