Gaussian filtering algorithm describing the topography of temper rolled strip and related edge effect

Xia, Chunyu; Li, Hongbo; Zhang, Jie; Zhang, Xin; She, Jia

doi:10.3139/120.111118

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…Linear filtering includes mean filtering and Gaussian filtering, which has been widely used in steel surface defect image denoising [31–36]. The mean filtering is simple and fast, but it cannot protect the image details well.…”

Section: Image Processing Algorithmmentioning

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: Image Processing Algorithmmentioning

confidence: 99%

“…Shi et al [30] proposed a measurement system to realize the simultaneous detection of 2D and 3D defects of rail defects, and improved the detection accuracy. denoising [31][32][33][34][35][36]. The mean filtering is simple and fast, but it cannot protect the image details well.…”

Section: Range Imagingmentioning

confidence: 99%

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

Tang

Chen

Sun

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…[19][20][21]. They are often represented by a Gaussian filter [22,23] and plenty of its modifications [24][25][26]. Spline filters are also very popular in the measurement and analysis of surface topography [27,28], e.g., two-dimensional isotropic spline filters [29], cubic spline filters [30], B-spline filtering techniques [31], or other combinations [32,33].…”

Section: Introductionmentioning

confidence: 99%

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

Podulka

2022

Sensors

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Errors that occur when surface topography is measured and analysed can be classified depending on the type of surface studied. Many types of surface topographies are considered when frequency-based errors are studied. However, turned surface topography is not comprehensively studied when data processing errors caused by false estimation (definition and suppression) of selected surface features (form or noise) are analysed. In the present work, the effects of the application of various methods (regular Gaussian regression, robust Gaussian regression, and spline and fast Fourier Transform filters) for the suppression of high-frequency measurement noise from the raw measured data of turned surface topography are presented and compared. The influence and usage of commonly used available commercial software, e.g., autocorrelation function, power spectral density, and texture direction, which function on the values of areal surface topography parameters from selected (ISO 25178) standards, are also introduced. Analysed surfaces were measured with a stylus or via non-contact (optical–white light interferometry) methods. It was found that the characterisation of surface topography, based on the analysis of selected features, can be crucial in reducing measurement and data analysis errors when various filters are applied. Moreover, the application of common functions can be advantageous when feature-based studies are proposed for both profile and areal data processing.

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Gaussian filtering algorithm describing the topography of temper rolled strip and related edge effect

Cited by 2 publications

References 23 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

Feature-Based Characterisation of Turned Surface Topography with Suppression of High-Frequency Measurement Errors

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