Automatic surface defect recognition for hot-rolled steel strip using AlexNet convolutional neural network

Boudiaf, Adel; Harrar, Khaled; Benlahmidi, Said; Zaghdoudi, Rachid; Ziani, Said; Taleb, Samira

doi:10.1109/ispa54004.2022.9786361

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Recently, deep learning algorithms have proven their efficiency in a large number of computer vision-based applications and have been successfully used for the classification of surface defects of hot-rolled products [19]. Several studies targeted surface defect classification adopting deep learning-based techniques, especially Convolutional Neural Networks (CNN).…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Steel surface defect recognition using classifier combination

Zaghdoudi,

Bouguettaya,

Boudiaf

2024

Int J Adv Manuf Technol

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The quality control of steel products' surface is of utmost importance, where several inspection techniques and technologies were proposed over the last few years. Traditional manual inspection procedures are facing several limitations and often fall short in ensuring flawlessness. Visionbased strategies for automatic steel surface inspection have emerged as powerful and effective tools to solve various industrial-related problems, including products quality control. Therefore, the current study aims to improve the recognition rate of steel surface defects classification system by introducing a novel classifier combination approach. The proposed system utilizes two distinct feature sets, namely LCCMSP and DCP, which were carefully selected based on a comprehensive comparative study of 19 state-of-the-art texture descriptors, considering both accuracy and time consumption. These generated features are individually fed to two classifiers, SVM and RF, leading to the creation of four base classifiers. In the final step, the Bayesian fusion rule is applied to integrate the outputs of these classifiers, ultimately providing the definitive classification decision. To evaluate the proposed system, two steel surface defects datasets, NEU-CLS and X-SDD, are utilized. The experimental results demonstrate that the proposed combination approach surpasses classical combination methods achieving remarkable outcomes compared to existing steel surface defects classification approaches. This highlights the effectiveness and superiority of the proposed system in accurately identifying and classifying steel surface defects while maintaining fast execution time.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Steel surface defect recognition using classifier combination

Zaghdoudi,

Bouguettaya,

Boudiaf

2024

Int J Adv Manuf Technol

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“…Various researchers in the steel surface defect inspection field have employed the k-nearest neighbors classifier [65,168]. For instance, in their work [165], Boudiaf et al developed an automatic system to detect surface defects in hot-rolled flat steel. Their method used the HOG for image feature extraction and a k-nearest neighbors classifier for defect classification, achieving a recognition accuracy of 91.12%.…”

Section: K-nearest Neighbors Classifiersmentioning

confidence: 99%

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

Ibrahim,

Tapamo

2024

Informatics

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In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual inspection technologies, particularly automation techniques, have been introduced to address these shortcomings. This paper conducts a thorough survey examining vision-based methodologies related to detecting and classifying surface defects on steel products. These methodologies encompass statistical, spectral, texture segmentation based methods, and machine learning-driven approaches. Furthermore, various classification algorithms, categorized into supervised, semi-supervised, and unsupervised techniques, are discussed. Additionally, the paper outlines the future direction of research focus.

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“…[54] Instead of training a model from scratch on a specific task, TL leverages the knowledge gained from solving a task to improve performance on a different one. TL has been successfully applied in various domains and applications also for the steel sector, such as image analysis, [55][56][57][58] natural language processing, [59] and speech recognition. [60] It allows models to benefit from previous knowledge and accelerate the development and deployment of ML systems.…”

Section: Structure Of the Jominy Profile Estimatormentioning

confidence: 99%

A Deep Learning‐Based Approach to the Estimation of Jominy Profile of Medium‐Carbon Quench Hardenable Steels

Colla,

Vannucci,

Matino

et al. 2023

steel research int.

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The possibility to estimate the Jominy profile of steel based on its chemical composition is of utmost importance and high practical relevance for industries, at enables a preliminary assessment of the suitability of a specific steel grade to a particular application or to the requirements of a customer, by saving time and resources as the Jominy end‐quench test is costly and time consuming. More importantly, an estimator can be used in steel grade design, by supporting the investigation of the most suitable chemistry to meet some given specifications. The paper proposes a novel approach to estimate the hardenability profile of medium Carbon quench hardenable steels, which exploits the potential of deep learning to correlate the steel metallurgy to the entire shape of the curve rather than to its single points, by thus being adaptable to a wide range of steel grades while providing very accurate estimates. Moreover, the proposed approach is suitable implement a transfer learning paradigm, as it can exploit the knowledge acquired by training on a specific dataset to adapt the model to different steel grades for which less data or data holding different features are available.This article is protected by copyright. All rights reserved.

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Automatic surface defect recognition for hot-rolled steel strip using AlexNet convolutional neural network

Cited by 7 publications

References 11 publications

Steel surface defect recognition using classifier combination

Steel surface defect recognition using classifier combination

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

A Deep Learning‐Based Approach to the Estimation of Jominy Profile of Medium‐Carbon Quench Hardenable Steels

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