Sensitivity analysis for texture models applied to rust steel classification

Trujillo, Maite; Sadki, Mustapha

doi:10.1117/12.526838

Cited by 7 publications

(4 citation statements)

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“…Usually, the corrosion process produces rough surfaces, and image analysis based on textural features can be used for quantification and discriminate corrosion extent and type. Several authors investigated automatic corrosion detection using image processing techniques [4][5][6][7]. Computer image processing involves definition and development of techniques and algorithms for processing pictorial data and its pixel value.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Corrosion Growth on SS304 Based on Textural and Color Features from Image Analysis

2013

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Corrosion surface damage in the form of pitting and microcracks is observed in many systems and affects the integrity of steel structures in nuclear, civil, and industrial engineering. In order to gain a better understanding and develop nondestructive and automatic detection/assessment of corrosion damage and its growth, an image analysis based on texture using wavelet transforms and color features was carried out. Experiments were conducted on steel 304 panels under three different electrolyte solutions, and periodic scans were used to obtain the images for analysis over time. The results obtained from the image analysis are presented to illustrate the metrics which best characterize early stage corrosion damage growth behavior. The results obtained indicate that textural features in combination with color features are more effective and may be used for correlating service/failure conditions based on corrosion morphology.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Corrosion Growth on SS304 Based on Textural and Color Features from Image Analysis

2013

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“…Corrosion is the destructive attack of a metal by chemical or electrochemical reaction with its environment [1]. The exposure of metallic surfaces structures to rust degradation during their operational life is a known problem and it affects storage tanks, steel bridges, pipelines, and ships [2].…”

Section: Introductionmentioning

confidence: 99%

“…The potentiality of image processing techniques for automatic rust steel detection is investigated in [2]. The methodology introduced an iterative multivariate data analysis to examine the effects of rust steel descriptors, that is, texture and color distribution on a set of classifier algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The methodology introduced an iterative multivariate data analysis to examine the effects of rust steel descriptors, that is, texture and color distribution on a set of classifier algorithms. In this analysis, a selector of classifiers indicates the algorithm that provides good classification results (high sensitivity) and acceptable time response for the automation of the system [2].…”

Section: Introductionmentioning

confidence: 99%

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On the Evaluation of Texture and Color Features for Nondestructive Corrosion Detection

Medeiros

Ramalho

Bento

et al. 2010

EURASIP J. Adv. Signal Process.

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We present a methodology for automatic corrosion detection in digital images of carbon steel storage tanks and pipelines from a petroleum refinery. The database consists of optical digital images taken from equipments exposed to marine atmosphere during their operational life. This new approach focuses on color and texture descriptors to accomplish corroded and noncorroded surface area discrimination. The performance of the proposed corrosion descriptors is evaluated by using Fisher linear discriminant analysis (FLDA). This approach presents two main advantages: No refinery stoppages are required and potential-related catastrophes can be prevented.

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Corrosion defect segmentation method based on superpixel feature cascade

Sun,

Li,

et al. 2024

Ain Shams Engineering Journal

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Sensitivity analysis for texture models applied to rust steel classification

Cited by 7 publications

References 0 publications

Evaluation of Corrosion Growth on SS304 Based on Textural and Color Features from Image Analysis

Evaluation of Corrosion Growth on SS304 Based on Textural and Color Features from Image Analysis

On the Evaluation of Texture and Color Features for Nondestructive Corrosion Detection

Corrosion defect segmentation method based on superpixel feature cascade

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