Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures
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 lightin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7600 |
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| author | Michał Bembenek Teodor Mandziy Iryna Ivasenko Olena Berehulyak Roman Vorobel Zvenomyra Slobodyan Liubomyr Ropyak |
| author_facet | Michał Bembenek Teodor Mandziy Iryna Ivasenko Olena Berehulyak Roman Vorobel Zvenomyra Slobodyan Liubomyr Ropyak |
| author_sort | Michał Bembenek |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:10:13Z |
| format | Article |
| id | doaj.art-8b4bf87c910648a68a8f415ab0256c26 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:13Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8b4bf87c910648a68a8f415ab0256c262023-11-23T21:51:54ZengMDPI AGSensors1424-82202022-10-012219760010.3390/s22197600Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal StructuresMichał Bembenek0Teodor Mandziy1Iryna Ivasenko2Olena Berehulyak3Roman Vorobel4Zvenomyra Slobodyan5Liubomyr Ropyak6Department of Manufacturing Systems, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Kraków, PolandDepartment of the Theory of Wave Processes and Optical Systems of Diagnostics, Karpenko Physico-Mechanical Institute of the NAS of Ukraine, 5 Naukova St., 79060 Lviv, UkraineDepartment of the Theory of Wave Processes and Optical Systems of Diagnostics, Karpenko Physico-Mechanical Institute of the NAS of Ukraine, 5 Naukova St., 79060 Lviv, UkraineDepartment of the Theory of Wave Processes and Optical Systems of Diagnostics, Karpenko Physico-Mechanical Institute of the NAS of Ukraine, 5 Naukova St., 79060 Lviv, UkraineDepartment of the Theory of Wave Processes and Optical Systems of Diagnostics, Karpenko Physico-Mechanical Institute of the NAS of Ukraine, 5 Naukova St., 79060 Lviv, UkraineDepartment of Corrosion and Corrosion Protection, Karpenko Physico-Mechanical Institute of the NAS of Ukraine, 5 Naukova St., 79060 Lviv, UkraineDepartment of Computerized Engineering, Ivano-Frankivsk National Technical University of Oil and Gas, 76019 Ivano-Frankivsk, UkraineThis 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.https://www.mdpi.com/1424-8220/22/19/7600level-set methodcolor image processingcoating damagerust detectionmulticlass image segmentation |
| spellingShingle | Michał Bembenek Teodor Mandziy Iryna Ivasenko Olena Berehulyak Roman Vorobel Zvenomyra Slobodyan Liubomyr Ropyak Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures Sensors level-set method color image processing coating damage rust detection multiclass image segmentation |
| title | Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures |
| title_full | Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures |
| title_fullStr | Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures |
| title_full_unstemmed | Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures |
| title_short | Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures |
| title_sort | multiclass level set segmentation of rust and coating damages in images of metal structures |
| topic | level-set method color image processing coating damage rust detection multiclass image segmentation |
| url | https://www.mdpi.com/1424-8220/22/19/7600 |
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