Defect Detection on the Surface of the Bellow Expansion Joints Based on Machine Vision Article Swipe
Fanwu Meng
,
Xiangyi Xiang
,
Di Wu
,
Tao Gong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3431635
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3431635
Since the surface quality of the expansion joints has a large impact on bellows performance, the detection of surface defects in expansion joints is an important aspect of their production process. This paper presents a two-step method for detecting unpredictable faults based on machine vision. The expansion joints are first segmented from the smooth parts of the bellows and background using a single Gabor filter. The second step is to split the segmented expansion joint into blocks using the Haar feature response, and then the features in each block extracted with the Haar feature are normalized to represent the surface condition of the block. A number of defect-free samples were used to obtain the normal range of normalized features. Experiments were performed on 10 mm diameter plastic bellows and compared with deep learning methods GAN and CFLOW-AD. The accuracy of our proposed method was verified according to experimental tests using 500 images from real samples. Results show that the proposed method can realize real-time detection with high accuracy in the industry.
Related Topics
Concepts
Bellows
Block (permutation group theory)
Feature (linguistics)
Computer science
Artificial intelligence
Joint (building)
Surface (topology)
Machine vision
Process (computing)
Expansion joint
Computer vision
Pattern recognition (psychology)
Materials science
Mathematics
Structural engineering
Composite material
Engineering
Geometry
Operating system
Metallurgy
Linguistics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3431635
- OA Status
- gold
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400877813
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400877813Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3431635Digital Object Identifier
- Title
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Defect Detection on the Surface of the Bellow Expansion Joints Based on Machine VisionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Fanwu Meng, Xiangyi Xiang, Di Wu, Tao GongList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3431635Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2024.3431635Direct OA link when available
- Concepts
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Bellows, Block (permutation group theory), Feature (linguistics), Computer science, Artificial intelligence, Joint (building), Surface (topology), Machine vision, Process (computing), Expansion joint, Computer vision, Pattern recognition (psychology), Materials science, Mathematics, Structural engineering, Composite material, Engineering, Geometry, Operating system, Metallurgy, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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