MD-TransUNet: An Image Segmentation Network for Car Front Face Design Article Swipe
Jinyan Ouyang
,
Hongru Shi
,
Jianning Su
,
Shutao Zhang
,
Aimin Zhou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14198688
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14198688
To enhance the segmentation accuracy of car front face elements such as headlights and grilles for car front face design, and to improve the superiority and efficiency of solutions in automotive partial modification design, this paper introduces MD-TransUNet, a semantic segmentation network based on the TransUNet model. MD-TransUNet integrates multi-scale attention gates and dynamic-channel graph convolution networks to enhance image restoration across various design drawings. To improve accuracy and detail retention in segmenting automotive front face elements, dynamic-channel graph convolution networks model global channel relationships between contextual sequences, thereby enhancing the Transformer’s channel encoding capabilities. Additionally, a multi-scale attention-based decoder structure is employed to restore feature map dimensions, mitigating the loss of detail in the local feature encoding by the Transformer. Experimental results demonstrate that the MSAG module significantly enhances the model’s ability to capture details, while the DCGCN module improves the segmentation accuracy of the shapes and edges of headlights and grilles. The MD-TransUNet model outperforms existing models on the automotive front face dataset, achieving mF-score, mIoU, and OA metrics of 95.81%, 92.08%, and 98.86%, respectively. Consequently, the MD-TransUNet model increases the precision of automotive front face element segmentation and achieves a more advanced and efficient approach to partial modification design.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14198688
- OA Status
- gold
- Cited By
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- References
- 33
- Related Works
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- OpenAlex ID
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https://openalex.org/W4402860978Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app14198688Digital Object Identifier
- Title
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MD-TransUNet: An Image Segmentation Network for Car Front Face DesignWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-26Full publication date if available
- Authors
-
Jinyan Ouyang, Hongru Shi, Jianning Su, Shutao Zhang, Aimin ZhouList of authors in order
- Landing page
-
https://doi.org/10.3390/app14198688Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/app14198688Direct OA link when available
- Concepts
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Computer science, Computer vision, Artificial intelligence, Face (sociological concept), Sociology, Social scienceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.92.08%, | 173 |
| abstract_inverted_index.95.81%, | 172 |
| abstract_inverted_index.98.86%, | 175 |
| abstract_inverted_index.ability | 132 |
| abstract_inverted_index.between | 85 |
| abstract_inverted_index.capture | 134 |
| abstract_inverted_index.channel | 83, 92 |
| abstract_inverted_index.decoder | 99 |
| abstract_inverted_index.design, | 19, 33 |
| abstract_inverted_index.design. | 201 |
| abstract_inverted_index.element | 188 |
| abstract_inverted_index.enhance | 1, 58 |
| abstract_inverted_index.feature | 105, 116 |
| abstract_inverted_index.grilles | 14 |
| abstract_inverted_index.improve | 22, 66 |
| abstract_inverted_index.metrics | 170 |
| abstract_inverted_index.network | 41 |
| abstract_inverted_index.partial | 31, 199 |
| abstract_inverted_index.restore | 104 |
| abstract_inverted_index.results | 122 |
| abstract_inverted_index.thereby | 88 |
| abstract_inverted_index.various | 62 |
| abstract_inverted_index.accuracy | 4, 67, 143 |
| abstract_inverted_index.achieves | 191 |
| abstract_inverted_index.advanced | 194 |
| abstract_inverted_index.approach | 197 |
| abstract_inverted_index.dataset, | 164 |
| abstract_inverted_index.details, | 135 |
| abstract_inverted_index.elements | 9 |
| abstract_inverted_index.employed | 102 |
| abstract_inverted_index.encoding | 93, 117 |
| abstract_inverted_index.enhances | 129 |
| abstract_inverted_index.existing | 157 |
| abstract_inverted_index.grilles. | 152 |
| abstract_inverted_index.improves | 140 |
| abstract_inverted_index.networks | 56, 80 |
| abstract_inverted_index.semantic | 39 |
| abstract_inverted_index.TransUNet | 45 |
| abstract_inverted_index.achieving | 165 |
| abstract_inverted_index.attention | 50 |
| abstract_inverted_index.drawings. | 64 |
| abstract_inverted_index.efficient | 196 |
| abstract_inverted_index.elements, | 76 |
| abstract_inverted_index.enhancing | 89 |
| abstract_inverted_index.increases | 181 |
| abstract_inverted_index.mF-score, | 166 |
| abstract_inverted_index.model’s | 131 |
| abstract_inverted_index.precision | 183 |
| abstract_inverted_index.retention | 70 |
| abstract_inverted_index.solutions | 28 |
| abstract_inverted_index.structure | 100 |
| abstract_inverted_index.automotive | 30, 73, 161, 185 |
| abstract_inverted_index.contextual | 86 |
| abstract_inverted_index.efficiency | 26 |
| abstract_inverted_index.headlights | 12, 150 |
| abstract_inverted_index.integrates | 48 |
| abstract_inverted_index.introduces | 36 |
| abstract_inverted_index.mitigating | 108 |
| abstract_inverted_index.segmenting | 72 |
| abstract_inverted_index.sequences, | 87 |
| abstract_inverted_index.convolution | 55, 79 |
| abstract_inverted_index.demonstrate | 123 |
| abstract_inverted_index.dimensions, | 107 |
| abstract_inverted_index.multi-scale | 49, 97 |
| abstract_inverted_index.outperforms | 156 |
| abstract_inverted_index.restoration | 60 |
| abstract_inverted_index.superiority | 24 |
| abstract_inverted_index.Experimental | 121 |
| abstract_inverted_index.MD-TransUNet | 47, 154, 179 |
| abstract_inverted_index.Transformer. | 120 |
| abstract_inverted_index.modification | 32, 200 |
| abstract_inverted_index.segmentation | 3, 40, 142, 189 |
| abstract_inverted_index.Additionally, | 95 |
| abstract_inverted_index.Consequently, | 177 |
| abstract_inverted_index.MD-TransUNet, | 37 |
| abstract_inverted_index.capabilities. | 94 |
| abstract_inverted_index.relationships | 84 |
| abstract_inverted_index.respectively. | 176 |
| abstract_inverted_index.significantly | 128 |
| abstract_inverted_index.Transformer’s | 91 |
| abstract_inverted_index.attention-based | 98 |
| abstract_inverted_index.dynamic-channel | 53, 77 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.60424282 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |