Development of an optical defect inspection algorithm based on an active contour model for large steel roller surfaces
Abstract
On-site measurements and defect detection are of great importance for precision ground steel rollers due to their large dimension and weight. In addition to dimensional error, form accuracy, surface roughness, and surface/sub-surface cracks, there also exist optical defect requirements for steel roller surfaces, e.g., speckles, chatter marks, or feed traces. Since rollers with optical defects will always duplicate the defect patterns onto the metal sheet or foil during rolling, it is necessary as well as significant to scrutinize the roller surface after grinding. In industrial practice, defects are investigated mainly by experienced engineers through naked-eye inspections along particular directions and under appropriate illumination conditions. This is usually subjective and inconsistent. In this paper, a machine vision system is developed, to add onto the roller grinder, that is capable of acquiring the roller's surface image with high and consistent quality. In addition, to identify defects with fuzzy boundaries, intensity inhomogeneity, and complex background textures, an improved segmentation algorithm is developed based on an active contour without edges model. Furthermore, qualitative and quantitative comparisons of the proposed algorithm with the Chan-Vese model, the local binary fitting model, and the globally signed region pressure force model are carried out. The comparisons prove that the proposed method performs with better accuracy and robustness for fuzzy and inhomogeneous defect segmentation and consumes generally less computational time.
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Yang et al. developed an ACM-based defect detection method without edges that can effectively segment defect features with inhomogeneous boundaries from complicated surface textures.
“Yang et al. [95] developed a ACM-based defect detection method without edges through incorporating a variable penalty term and a convolution kernel, authors reported that it can effectively segment defect features with inhomogeneous boundaries from complicated surface textures.”
From Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020) · cites this paper as [95]
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Automated Visual Defect Detection for Flat Steel Surface: A Survey
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Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
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- Authors
- Yang, J., Li, X., Xu, J., Cao, Y., Zhang, Y., Wang, L., & Jiang, S.
- Affiliations
- Tsinghua University (CN)
- Corresponding author
- Yang, Jun
- Li, Xuekun
- Xu, Jirui
- Cao, Yuzhong
- Zhang, Yun
- Wang, Liping
- Jiang, Sheng
- Venue
- Applied Optics
- Publisher
- Optica Publishing Group
- Published
- 2018-03-27
- Volume
- 57
- Issue
- 10
- Pages
- 2490
- ISSN
- 0003-6935 (ISSN-L)
- 1559-128X
- 2155-3165
- OpenAlex
- W2790742704
- Cited by
- 29
- References
- 25
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 7 · 2019: 1
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Optics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (NSFC) (NSAF-U1430116)
- Ministry of Science and Technology of the People’s Republic of China (MOST) (2017ZX04007001)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- steel rollers, optical defect detection, active contours without edges, image segmentation, automated visual inspection, speckle, chatter marks, fuzzy boundaries, intensity inhomogeneity, Chan-Vese model, local binary fitting
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