MLMA-Net: multi-level multi-attentional learning for multi-label object detection in textile defect images Article Swipe
Bing Wei
,
Kuangrong Hao
,
Lei Gao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.00376
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.00376
For the sake of recognizing and classifying textile defects, deep learning-based methods have been proposed and achieved remarkable success in single-label textile images. However, detecting multi-label defects in a textile image remains challenging due to the coexistence of multiple defects and small-size defects. To address these challenges, a multi-level, multi-attentional deep learning network (MLMA-Net) is proposed and built to 1) increase the feature representation ability to detect small-size defects; 2) generate a discriminative representation that maximizes the capability of attending the defect status, which leverages higher-resolution feature maps for multiple defects. Moreover, a multi-label object detection dataset (DHU-ML1000) in textile defect images is built to verify the performance of the proposed model. The results demonstrate that the network extracts more distinctive features and has better performance than the state-of-the-art approaches on the real-world industrial dataset.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.00376
- https://arxiv.org/pdf/2102.00376
- OA Status
- green
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3128311366
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https://openalex.org/W3128311366Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.00376Digital Object Identifier
- Title
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MLMA-Net: multi-level multi-attentional learning for multi-label object detection in textile defect imagesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-31Full publication date if available
- Authors
-
Bing Wei, Kuangrong Hao, Lei GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2102.00376Publisher landing page
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-
https://arxiv.org/pdf/2102.00376Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2102.00376Direct OA link when available
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Textile, Net (polyhedron), Object (grammar), Artificial intelligence, Object based, Computer science, Computer vision, Pattern recognition (psychology), Mathematics, Materials science, Composite material, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2022: 1Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Moreover, | 91 |
| abstract_inverted_index.attending | 79 |
| abstract_inverted_index.detecting | 24 |
| abstract_inverted_index.detection | 95 |
| abstract_inverted_index.leverages | 84 |
| abstract_inverted_index.maximizes | 75 |
| abstract_inverted_index.(MLMA-Net) | 53 |
| abstract_inverted_index.approaches | 129 |
| abstract_inverted_index.capability | 77 |
| abstract_inverted_index.industrial | 133 |
| abstract_inverted_index.real-world | 132 |
| abstract_inverted_index.remarkable | 17 |
| abstract_inverted_index.small-size | 41, 67 |
| abstract_inverted_index.challenges, | 46 |
| abstract_inverted_index.challenging | 32 |
| abstract_inverted_index.classifying | 6 |
| abstract_inverted_index.coexistence | 36 |
| abstract_inverted_index.demonstrate | 114 |
| abstract_inverted_index.distinctive | 120 |
| abstract_inverted_index.multi-label | 25, 93 |
| abstract_inverted_index.performance | 107, 125 |
| abstract_inverted_index.recognizing | 4 |
| abstract_inverted_index.(DHU-ML1000) | 97 |
| abstract_inverted_index.multi-level, | 48 |
| abstract_inverted_index.single-label | 20 |
| abstract_inverted_index.discriminative | 72 |
| abstract_inverted_index.learning-based | 10 |
| abstract_inverted_index.representation | 63, 73 |
| abstract_inverted_index.state-of-the-art | 128 |
| abstract_inverted_index.higher-resolution | 85 |
| abstract_inverted_index.multi-attentional | 49 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |