Surface defect identification of aluminium strips with non-subsampled shearlet transform

Liu, Xiaoming; Xu, Ke; Zhou, Peng; Dongdong, Zhou; Zhou, Yujie

doi:10.1016/j.optlaseng.2019.105986

Cited by 17 publications

(7 citation statements)

References 21 publications

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“…Figure 3 illustrates online detection technologies used in China’s steel industry. The detection technology development direction is to use advanced modern detection technologies such as machine vision [ 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 ], laser-induced breakdown spectroscopy (LIBS) [ 94 , 95 , 96 , 97 , 98 ], ultrasonic microscopy technology, and others, in conjunction with deep-learning algorithms and statistical modeling theory; to apply or develop intelligent perception technology on the production line; and to conduct online or rapid detection of key parameters throughout the manufacturing process. The ultimate purposes of online detecting technologies are to provide intelligent management and process optimization in the steel industry, improve the quality of terminal products, increase labor productivity and reduce labor costs, and provide vital fundamental data for quality control and big data platforms.…”

Section: Key Technologies For Intelligent Manufacturing In Steel Indu...mentioning

confidence: 99%

Intelligent Manufacturing Technology in the Steel Industry of China: A Review

Zhou

et al. 2022

Sensors

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Intelligent manufacturing, defined as the integration of manufacturing with modern information technologies such as 5G, digitalization, networking, and intelligence, has grown in popularity as a means of boosting the productivity, intelligence, and flexibility of traditional manufacturing processes. The steel industry is a necessary support for modern life and economic development, and the Chinese steel industry’s capacity has expanded to roughly half of global production. However, the Chinese steel industry is now confronted with high labor costs, massive carbon emissions, a low level of intelligence, low production efficiency, and unstable quality control. Therefore, China’s steel industry has launched several large-scale intelligent manufacturing initiatives to improve production efficiency, product quality, manual labor intensity, and employee working conditions. Unfortunately, there is no comprehensive overview of intelligent manufacturing in China’s steel industry. We began this research by summarizing the construction goals and overall framework for intelligent manufacturing of the steel industry in China. Following that, we offered a brief review of intelligent manufacturing for China’s steel industry, as well as descriptions of two typical intelligent manufacturing models. Finally, some major technologies employed for intelligent production in China’s steel industry were introduced. This research not only helps to comprehend the development model, essential technologies, and construction techniques of intelligent manufacturing in China’s steel industry, but it also provides vital inspiration for the manufacturing industry’s digital and intelligence updates and quality improvement.

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Section: Key Technologies For Intelligent Manufacturing In Steel Indu...mentioning

confidence: 99%

Intelligent Manufacturing Technology in the Steel Industry of China: A Review

Zhou

et al. 2022

Sensors

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“…The ST provides efficient multiscale directional representation, which is a relatively new MGA method. Compared to other methods, it sets up disparate direction amounts at diverse decomposition scales and is preeminent when approximating 2D smooth functions with discontinuities along the C2-curves; the ST is fit to analyze images with complicated backgrounds and has been successfully applied to defecting steel defects in [101,102].…”

Section: ) Shearlet Transform (St)mentioning

confidence: 99%

“…SVM is a generalized linear classifier for binary classification, which has been widely used for the defect classification of flat steel surfaces [67,[100][101][102]118]. For the multiclass classification problem, Zhang et al [59] succeeded in identifying seven classes of steel surface defects effectively based on a multiclass SVM by simultaneously optimizing the kernel function selection and parameter settings of the traditional SVM method.…”

Section: ) Classifiers Based On Svmmentioning

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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“…The process generally includes image pre-processing, region of interest (RoI) searching, feature extraction, feature selection, and pattern recognition. In most of the work, feature extraction relies on hand-crafted features, including approaches such as statistical methods [4,5], spectral methods [6,7], and model-based methods [8,9]. However, the approaches rely on a great deal of prior knowledge and design experience from experts [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel ST-YOLO Network for Steel-Surface-Defect Detection

Ma,

Zhang,

Zhao

2023

Sensors

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Recent progress has been made in defect detection using methods based on deep learning, but there are still formidable obstacles. Defect images have rich semantic levels and diverse morphological features, and the model is dynamically changing due to ongoing learning. In response to these issues, this article proposes a shunt feature fusion model (ST-YOLO) for steel-defect detection, which uses a split feature network structure and a self-correcting transmission allocation method for training. The network structure is designed to specialize the process of classification and localization tasks for different computing needs. By using the self-correction criteria of adaptive sampling and dynamic label allocation, more sufficiently high-quality samples are utilized to adjust data distribution and optimize the training process. Our model achieved better performance on the NEU-DET datasets and the GC10-DET datasets and was validated to exhibit excellent performance.

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Surface defect identification of aluminium strips with non-subsampled shearlet transform

Cited by 17 publications

References 21 publications

Intelligent Manufacturing Technology in the Steel Industry of China: A Review

Intelligent Manufacturing Technology in the Steel Industry of China: A Review

Automated Visual Defect Classification for Flat Steel Surface: A Survey

A Novel ST-YOLO Network for Steel-Surface-Defect Detection

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