Segmentation of rust defects on painted steel surfaces by intelligent image analysis

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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“…Vorobel et al. [50] and Nand et al. [51] developed of Retinex method to remove non‐uniform illumination for steel surface images.…”

Section: Image Processing Algorithmmentioning

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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“…Our previous works [ 70 , 71 , 72 , 73 , 74 ] were dedicated to rust damage segmentation under conditions that can distort its damage percentage assessment. They were focused on the detection of one type of damage—rust.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures

Bembenek

Mandziy

Ivasenko

et al. 2022

Sensors

Self Cite

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This paper describes the combined detection of coating and rust damages on painted metal structures through the multiclass image segmentation technique. Our prior works were focused solely on the localization of rust damages and rust segmentation under different ambient conditions (different lighting conditions, presence of shadows, low background/object color contrast). This paper method proposes three types of damages: coating crack, coating flaking, and rust damage. Background, paint flaking, and rust damage are objects that can be separated in RGB color-space alone. For their preliminary classification SVM is used. As for paint cracks, color features are insufficient for separating it from other defect types as they overlap with the other three classes in RGB color space. For preliminary paint crack segmentation we use the valley detection approach, which analyses the shape of defects. A multiclass level-set approach with a developed penalty term is used as a framework for the advanced final damage segmentation stage. Model training and accuracy assessment are fulfilled on the created dataset, which contains input images of corresponding defects with respective ground truth data provided by the expert. A quantitative analysis of the accuracy of the proposed approach is provided. The efficiency of the approach is demonstrated on authentic images of coated surfaces.

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“…Compared to epoxy paints developed for aggressive saline environments, with a cost 6 times higher than alkyd paints, the latter are the most widely ones used at home, even in saline and/or humid environments in developing countries [10,11]. The use of alkyd paint leads to great economic losses, since the anticorrosive-alkyd paint system provides resistance to environments of only a medium corrosive category, due to its low adhesion [13][14][15]. In a saline or humid environment, the degradation process of this type of paint is accelerated by the formation of cracks and blisters, which is directly associated with the traditionallyused anticorrosive [15,16].…”

Section: Introductionmentioning

confidence: 99%

Increase Corrosion Resistance of Alkyd Paint With Nio-zro2y Coating on Hot-rolled Steel

Cano

Malagon

González

2022

Preprint

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The corrosion resistance of an alkyd paint was evaluated, substituting commercial anticorrosive with a thin film of NiO-ZrO2Y partially stabilized with Ytria deposited by means of the RF sputtering technique. Hot-rolled (HR) steel was used as the substrate. For the two anticorrosive coating systems, the structure, morphology, and composition were determined via the techniques of X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPS) in order to determine the composition of the NiO-ZrO2Y film. The corrosion resistance was determined from polarization curves and weight loss by immersion in a 3.5% NaCl solution. The electrochemical and weight loss results indicated that use of the NiO-ZrO2 film shows a fourfold increase in the corrosion resistance of the material subjected to immersion in the corrosive electrolyte compared to the traditionally-used commercial anticorrosive.

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Segmentation of rust defects on painted steel surfaces by intelligent image analysis

Cited by 31 publications

References 30 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

Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures

Increase Corrosion Resistance of Alkyd Paint With Nio-zro2y Coating on Hot-rolled Steel

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