Influence of Colour Restoration on Rust Image Segmentation

Mandziy, Teodor; Ivasenko, Iryna; Berehulyak, Olena; Vorobel, Roman

doi:10.1109/ukrcon53503.2021.9575407

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Modern methods of image processing are often based on advanced mathematical theories [85], among them differential equations [86], algebraic models with logarithmic transformations [87,88], сalculus of variations and optimization methods [89]. Such processing methods often consist in segmentation of regions of interest and use different segmentation algorithms depending on the type of problem being solved [90,91] and the conditions of image registration [92,93]. The preprocessing step can forego segmentation with the aim to eliminate noise [94] or enhance the quality [95] of the input image.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Degree of Degradation of Brake Pad Friction Surfaces Using Image Processing

Mandziy,

Ivasenko,

Berehulyak

et al. 2024

Lubricants

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The improvement of drilling rig systems to ensure a reduction in unproductive time spent on lowering and lifting operations for replacing drilling tools and restoring the performance of drilling equipment units is an important task. At the same time, considerable attention is paid to the reliable and efficient operation of the braking systems of drilling rig winches. In the process of operation, the polymer pads periodically come into contact with the outer cylindrical surface of the metal pulley during braking, work in extreme conditions and wear out intensively, so they need periodic replacement. Tests were carried out on a modernized stand and in industrial conditions for the brakes of drilling winches. A methodology for evaluating the degradation of the brake pad friction surface during its operation is proposed. The assessment of the degradation degree is carried out based on the image of the brake pad surface using image processing techniques. Geometric transformations of the input image were performed to avoid perspective distortions caused by the concave shape of the brake pads and the spatial angle at which the image is acquired to avoid glares. The crack detection step was implemented based on the scale-space theory, followed by contour detection and skeletonization. The ratios of the area and perimeter of segmented and skeletonized cracks to the total area were chosen as integral characteristics of the degradation degree. With the help of scanning electron microscopy, the character of the destruction of the friction surface and the degradation of the polymer material was investigated. Experimental studies were performed, and the application of the proposed method is illustrated.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Degree of Degradation of Brake Pad Friction Surfaces Using Image Processing

Mandziy,

Ivasenko,

Berehulyak

et al. 2024

Lubricants

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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%

“…They were focused on the detection of one type of damage—rust. In particular, rust detection based on HSV image color model [ 71 ], the use of the single-scaled retinex method [ 72 ], the influence of color restoration based on a color checker [ 73 ], and the reduction of shadow effects [ 74 ] algebraic model with an asymmetric characteristic [ 75 ] and methods of logarithmic type image processing [ 76 ] as well as the application of inhomogeneity inforced piecewise smooth model [ 77 ] for image segmentation were considered. A dimensionality reduction PCA technique was applied in [ 78 ] for the rust segmentation in images obtained in irregular lighting conditions.…”

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

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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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