Detection of Pinholes in Steel Slabs Using Gabor Filter Combination and Morphological Features

Choi, Doo-chul; Jeon, Yong-Ju; Kim, Seung Hun; Moon, Seokbae; Yun, Jong Pil; Kim, Sang Woo

doi:10.2355/isijinternational.isijint-2016-160

Cited by 43 publications

(19 citation statements)

References 23 publications

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“…The real part and imaginary part of the typical Gabor detector can be used for image smoothing and edge detection respectively, whose parameters are mainly determined by the size and direction of defects, and a single Gabor filter is hard to obtain ideal results for mixed defects of various sizes. Choi et al [ 72 ] proposed a dual Gabor filter combination method based on morphological feature enhancement to identify small holes in the steel plates. Medina et al [ 73 ] fused Gabor features with other classic image features and greatly improved the image defect detection accuracy.…”

Section: Taxonomy Of Two-dimension Defect Detection Methodsmentioning

confidence: 99%

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Fang

Luo

Zhou

et al. 2020

Sensors

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The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

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Section: Taxonomy Of Two-dimension Defect Detection Methodsmentioning

confidence: 99%

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Fang

Luo

Zhou

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…The parameters of Gabor filter are mainly decided by the defect size and direction, it is thus hard to obtain desirable results for miscellaneous defects with various sizes by a single Gabor filter. Accordingly, Choi et al [64] proposed a two-Gabor-filter combinational method enhanced by morphological features to separate pinholes on steel slabs. Similarly, Medina et al [65] claimed that the correct defection rate could be increased by fusing Gabor features to other classical image features to a large extent.…”

Section: ) Gabor Filtersmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

339

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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“…Therefore, steel surface defect detection is a crucial part of steel quality management. The computer image processing method is a kind of steel detection method [1], [2], by which many detection algorithms are formed. The traditional detection algorithms based on feature extraction of artificial experience can detect simple and fast, but it only can detect a single goal and use it in a specific circumstance [3]- [11].…”

Section: Introductionmentioning

confidence: 99%

A Steel Surface Defect Recognition Algorithm Based on Improved Deep Learning Network Model Using Feature Visualization and Quality Evaluation

2020

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Steel defect detection is used to detect defects on the surface of the steel and to improve the quality of the steel surface. However, traditional image detection algorithms cannot meet the detection requirements because of small defect features and low contrast between background and features about steel surface defect datasets. A novel recognition algorithm for steel surface defects based on improved deep learning network models using feature visualization and quality evaluation is proposed in this paper. Firstly, the VGG19 is used to pre-train the steel surface defect classification task and the corresponding DVGG19 is established to extract the feature images in different layers from defects weight model. Secondly, the SSIM and decision tree are used to evaluate the feature image quality and adjust the parameters and structure of VGG19. On this basis, a new VSD network is obtained and used for the classification of steel surface defects. Comparing with ResNet and VGG19 methods, experiment results show that the proposed method markedly can improve the average accuracy of classification, and the model is able to converge quickly, which can be good for steel surface defect recognition using VSD network model of feature visualization and quality evaluation.

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Detection of Pinholes in Steel Slabs Using Gabor Filter Combination and Morphological Features

Cited by 43 publications

References 23 publications

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Automated Visual Defect Detection for Flat Steel Surface: A Survey

A Steel Surface Defect Recognition Algorithm Based on Improved Deep Learning Network Model Using Feature Visualization and Quality Evaluation

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