Metal Surface Defect Detection Based on a Transformer with Multi-Scale Mask Feature Fusion Article Swipe
Lin Zhao
,
Yu Zheng
,
Tao Peng
,
Zheng En-rang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23239381
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23239381
In the production process of metal industrial products, the deficiencies and limitations of existing technologies and working conditions can have adverse effects on the quality of the final products, making surface defect detection particularly crucial. However, collecting a sufficient number of samples of defective products can be challenging. Therefore, treating surface defect detection as a semi-supervised problem is appropriate. In this paper, we propose a method based on a Transformer with pruned and merged multi-scale masked feature fusion. This method learns the semantic context from normal samples. We incorporate the Vision Transformer (ViT) into a generative adversarial network to jointly learn the generation in the high-dimensional image space and the inference in the latent space. We use an encoder–decoder neural network with long skip connections to capture information between shallow and deep layers. During training and testing, we design block masks of different scales to obtain rich semantic context information. Additionally, we introduce token merging (ToMe) into the ViT to improve the training speed of the model without affecting the training results. In this paper, we focus on the problems of rust, scratches, and other defects on the metal surface. We conduct various experiments on five metal industrial product datasets and the MVTec AD dataset to demonstrate the superiority of our method.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23239381
- https://www.mdpi.com/1424-8220/23/23/9381/pdf?version=1700811924
- OA Status
- gold
- Cited By
- 8
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388974804
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388974804Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23239381Digital Object Identifier
- Title
-
Metal Surface Defect Detection Based on a Transformer with Multi-Scale Mask Feature FusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-24Full publication date if available
- Authors
-
Lin Zhao, Yu Zheng, Tao Peng, Zheng En-rangList of authors in order
- Landing page
-
https://doi.org/10.3390/s23239381Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/23/23/9381/pdf?version=1700811924Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/23/23/9381/pdf?version=1700811924Direct OA link when available
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Computer science, Transformer, Artificial intelligence, Inference, Deep learning, Encoder, Artificial neural network, Pattern recognition (psychology), Machine learning, Feature vector, Data mining, Engineering, Electrical engineering, Operating system, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
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| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1424-8220/23/23/9381/pdf?version=1700811924 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s23239381 |
| publication_date | 2023-11-24 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2899280016, https://openalex.org/W2913074553, https://openalex.org/W4206947990, https://openalex.org/W6853228104, https://openalex.org/W4382317689, https://openalex.org/W4376607576, https://openalex.org/W6790908119, https://openalex.org/W4294651314, https://openalex.org/W3169651898, https://openalex.org/W4387682077, https://openalex.org/W2886479392, https://openalex.org/W2599354622, https://openalex.org/W3160074056, https://openalex.org/W2941813310, https://openalex.org/W2914570111, https://openalex.org/W2803446235, https://openalex.org/W2978971541, https://openalex.org/W3173538657, https://openalex.org/W4221140561, https://openalex.org/W3209793239, https://openalex.org/W4214694907, https://openalex.org/W3208797540, https://openalex.org/W4312772600, https://openalex.org/W3099658315, https://openalex.org/W4386047807, https://openalex.org/W4385801014, https://openalex.org/W3168124404, https://openalex.org/W4312290555, https://openalex.org/W4312849330, https://openalex.org/W4312910119, https://openalex.org/W3118600296, https://openalex.org/W1514907448, https://openalex.org/W2036705578, https://openalex.org/W4287887190, https://openalex.org/W3204520143, https://openalex.org/W2963045681, https://openalex.org/W3133719425, https://openalex.org/W4378714868 |
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| abstract_inverted_index.is | 57 |
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| abstract_inverted_index.we | 62, 137, 151, 175 |
| abstract_inverted_index.ViT | 158 |
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| abstract_inverted_index.can | 18, 45 |
| abstract_inverted_index.our | 210 |
| abstract_inverted_index.the | 1, 8, 23, 26, 81, 89, 101, 104, 109, 112, 157, 161, 165, 169, 178, 187, 201, 207 |
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| abstract_inverted_index.This | 78 |
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| abstract_inverted_index.model | 166 |
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| abstract_inverted_index.Vision | 90 |
| abstract_inverted_index.defect | 31, 51 |
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| abstract_inverted_index.latent | 113 |
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| abstract_inverted_index.crucial. | 34 |
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.87659514 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |