Binary Gabor pattern (BGP) descriptor and principal component analysis (PCA) for steel surface defects classification

Zaghdoudi, Rachid; Séridi, Hamid; Boudiaf, Adel; Ziani, Said

doi:10.1109/icaase51408.2020.9380108

Cited by 19 publications

(16 citation statements)

References 17 publications

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“…PCA is a linear combination method, which is widely used in feature dimension reduction of steel surface defects [33,122,128,129]. This method is simple, however, in many cases, the features are non-linear.…”

Section: Transform Domain Featuresmentioning

confidence: 99%

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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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Section: Transform Domain Featuresmentioning

confidence: 99%

“…SVM shows many unique advantages in solving small sample, non-linear and high-dimensional pattern recognition, and has been widely used in steel surface defect detection [16,18,113,121,123,124,128].…”

Section: Support Vector Machine (Svm)mentioning

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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“…The proposed DST-GLCM approach was able to achieve a classification rate of 96%. Zaghdoudi et al [5] proposed an approach based on the Binary Gabor Pattern (BGP) to extract significant features of hot-rolled steel strips' surface defects. Then, after applying the Principal Component Analysis (PCA) technique, the reduced features were fed to an SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Deep ensemble transfer learning-based approach for classifying hot-rolled steel strips surface defects

Bouguettaya

Mentouri²,

Zarzour³

2023

Int J Adv Manuf Technol

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Over the last few years, advanced deep learning-based computer vision algorithms are revolutionizing the manufacturing field. Thus, several industry-related hard problems can be solved by training these algorithms, including flaw detection in various materials. Therefore, identifying steel surface defects is considered one of the most important tasks in the steel industry. In this paper, we propose a deep learning-based model to classify six of the most common steel strip surface defects using the NEU-CLS dataset. We investigate the effectiveness of two state-of-the-art CNN architectures (MobileNet-V2 and Xception) combined with the transfer learning approach. The proposed approach uses an ensemble of two pre-trained state-of-the-art Convolutional Neural Networks, which are MobileNet-V2 and Xception. To perform a comparative analysis of the proposed architectures, several evaluation metrics are adopted, including loss, accuracy, precision, recall, F1-score, and execution time. The experimental results show that the proposed deep ensemble learning approach provides higher performance achieving an accuracy of 99.72% compared to MobileNet-V2 (98.61%) and Xception (99.17%) while preserving fast execution time and small models' size.

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“…But the recognition accuracy was low. Zaghdoudi et al (2020) used the dimensionality reduction program based on principal EC 40,6 component analysis to obtain the compact representation of the defect image and the SVM multi-class classifier to give the final decision. However, these methods are based on the open dataset NEU-DET, and there is no field test.…”

Section: Introductionmentioning

confidence: 99%

Steel plate surface defect classification technology based on image enhancement and combination feature extraction

Yang

Huang

Ren

et al. 2023

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PurposeIn the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.Design/methodology/approach(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.FindingsWhen applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.Originality/valueIn brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

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Binary Gabor pattern (BGP) descriptor and principal component analysis (PCA) for steel surface defects classification

Cited by 19 publications

References 17 publications

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

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

Deep ensemble transfer learning-based approach for classifying hot-rolled steel strips surface defects

Steel plate surface defect classification technology based on image enhancement and combination feature extraction

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