Vehicle Classification Algorithm Based on Improved Vision Transformer Article Swipe
Xinlong Dong
,
Peicheng Shi
,
Yueyue Tang
,
Li Yang
,
Aixi Yang
,
Taonian Liang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/wevj15080344
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/wevj15080344
Vehicle classification technology is one of the foundations in the field of automatic driving. With the development of deep learning technology, visual transformer structures based on attention mechanisms can represent global information quickly and effectively. However, due to direct image segmentation, local feature details and information will be lost. To solve this problem, we propose an improved vision transformer vehicle classification network (IND-ViT). Specifically, we first design a CNN-In D branch module to extract local features before image segmentation to make up for the loss of detail information in the vision transformer. Then, in order to solve the problem of misdetection caused by the large similarity of some vehicles, we propose a sparse attention module, which can screen out the discernible regions in the image and further improve the detailed feature representation ability of the model. Finally, this paper uses the contrast loss function to further increase the intra-class consistency and inter-class difference of classification features and improve the accuracy of vehicle classification recognition. Experimental results show that the accuracy of the proposed model on the datasets of vehicle classification BIT-Vehicles, CIFAR-10, Oxford Flower-102, and Caltech-101 is higher than that of the original vision transformer model. Respectively, it increased by 1.3%, 1.21%, 7.54%, and 3.60%; at the same time, it also met a certain real-time requirement to achieve a balance of accuracy and real time.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/wevj15080344
- OA Status
- gold
- Cited By
- 7
- References
- 35
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4401111520Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/wevj15080344Digital Object Identifier
- Title
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Vehicle Classification Algorithm Based on Improved Vision TransformerWork title
- Type
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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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2024-07-30Full publication date if available
- Authors
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Xinlong Dong, Peicheng Shi, Yueyue Tang, Li Yang, Aixi Yang, Taonian LiangList of authors in order
- Landing page
-
https://doi.org/10.3390/wevj15080344Publisher 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/wevj15080344Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Segmentation, Transformer, Pattern recognition (psychology), Machine learning, Engineering, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2979917379, https://openalex.org/W4399527199, https://openalex.org/W4400278868, https://openalex.org/W4400233986, https://openalex.org/W4392388527, https://openalex.org/W2166251495, https://openalex.org/W2144426297, https://openalex.org/W2962949934, https://openalex.org/W4205561327, https://openalex.org/W3131079466, https://openalex.org/W4248388475, https://openalex.org/W6739901393, https://openalex.org/W2971492022, https://openalex.org/W2586934430, https://openalex.org/W2917452308, https://openalex.org/W2916892474, https://openalex.org/W4280527735, https://openalex.org/W6851916956, https://openalex.org/W4365420262, https://openalex.org/W2524771588, https://openalex.org/W3131500599, https://openalex.org/W2804902458, https://openalex.org/W4214669216, https://openalex.org/W3137278571, https://openalex.org/W3138516171, https://openalex.org/W3160694286, https://openalex.org/W4214588794, https://openalex.org/W4304942965, https://openalex.org/W6849353692, https://openalex.org/W2097117768, https://openalex.org/W2194775991, https://openalex.org/W4313007769, https://openalex.org/W2962858109, https://openalex.org/W4319069095, https://openalex.org/W4366396441 |
| referenced_works_count | 35 |
| abstract_inverted_index.D | 69 |
| abstract_inverted_index.a | 67, 111, 212, 218 |
| abstract_inverted_index.To | 49 |
| abstract_inverted_index.an | 55 |
| abstract_inverted_index.at | 205 |
| abstract_inverted_index.be | 47 |
| abstract_inverted_index.by | 102, 199 |
| abstract_inverted_index.in | 8, 88, 93, 122 |
| abstract_inverted_index.is | 3, 186 |
| abstract_inverted_index.it | 197, 209 |
| abstract_inverted_index.of | 5, 11, 17, 85, 99, 106, 133, 153, 160, 170, 177, 190, 220 |
| abstract_inverted_index.on | 25, 174 |
| abstract_inverted_index.to | 37, 72, 79, 95, 144, 216 |
| abstract_inverted_index.up | 81 |
| abstract_inverted_index.we | 53, 64, 109 |
| abstract_inverted_index.and | 33, 44, 125, 150, 156, 184, 203, 222 |
| abstract_inverted_index.can | 28, 116 |
| abstract_inverted_index.due | 36 |
| abstract_inverted_index.for | 82 |
| abstract_inverted_index.met | 211 |
| abstract_inverted_index.one | 4 |
| abstract_inverted_index.out | 118 |
| abstract_inverted_index.the | 6, 9, 15, 83, 89, 97, 103, 119, 123, 128, 134, 140, 147, 158, 168, 171, 175, 191, 206 |
| abstract_inverted_index.With | 14 |
| abstract_inverted_index.also | 210 |
| abstract_inverted_index.deep | 18 |
| abstract_inverted_index.loss | 84, 142 |
| abstract_inverted_index.make | 80 |
| abstract_inverted_index.real | 223 |
| abstract_inverted_index.same | 207 |
| abstract_inverted_index.show | 166 |
| abstract_inverted_index.some | 107 |
| abstract_inverted_index.than | 188 |
| abstract_inverted_index.that | 167, 189 |
| abstract_inverted_index.this | 51, 137 |
| abstract_inverted_index.uses | 139 |
| abstract_inverted_index.will | 46 |
| abstract_inverted_index.1.3%, | 200 |
| abstract_inverted_index.Then, | 92 |
| abstract_inverted_index.based | 24 |
| abstract_inverted_index.field | 10 |
| abstract_inverted_index.first | 65 |
| abstract_inverted_index.image | 39, 77, 124 |
| abstract_inverted_index.large | 104 |
| abstract_inverted_index.local | 41, 74 |
| abstract_inverted_index.lost. | 48 |
| abstract_inverted_index.model | 173 |
| abstract_inverted_index.order | 94 |
| abstract_inverted_index.paper | 138 |
| abstract_inverted_index.solve | 50, 96 |
| abstract_inverted_index.time, | 208 |
| abstract_inverted_index.time. | 224 |
| abstract_inverted_index.which | 115 |
| abstract_inverted_index.1.21%, | 201 |
| abstract_inverted_index.3.60%; | 204 |
| abstract_inverted_index.7.54%, | 202 |
| abstract_inverted_index.CNN-In | 68 |
| abstract_inverted_index.Oxford | 182 |
| abstract_inverted_index.before | 76 |
| abstract_inverted_index.branch | 70 |
| abstract_inverted_index.caused | 101 |
| abstract_inverted_index.design | 66 |
| abstract_inverted_index.detail | 86 |
| abstract_inverted_index.direct | 38 |
| abstract_inverted_index.global | 30 |
| abstract_inverted_index.higher | 187 |
| abstract_inverted_index.model. | 135, 195 |
| abstract_inverted_index.module | 71 |
| abstract_inverted_index.screen | 117 |
| abstract_inverted_index.sparse | 112 |
| abstract_inverted_index.vision | 57, 90, 193 |
| abstract_inverted_index.visual | 21 |
| abstract_inverted_index.Vehicle | 0 |
| abstract_inverted_index.ability | 132 |
| abstract_inverted_index.achieve | 217 |
| abstract_inverted_index.balance | 219 |
| abstract_inverted_index.certain | 213 |
| abstract_inverted_index.details | 43 |
| abstract_inverted_index.extract | 73 |
| abstract_inverted_index.feature | 42, 130 |
| abstract_inverted_index.further | 126, 145 |
| abstract_inverted_index.improve | 127, 157 |
| abstract_inverted_index.module, | 114 |
| abstract_inverted_index.network | 61 |
| abstract_inverted_index.problem | 98 |
| abstract_inverted_index.propose | 54, 110 |
| abstract_inverted_index.quickly | 32 |
| abstract_inverted_index.regions | 121 |
| abstract_inverted_index.results | 165 |
| abstract_inverted_index.vehicle | 59, 161, 178 |
| abstract_inverted_index.Finally, | 136 |
| abstract_inverted_index.However, | 35 |
| abstract_inverted_index.accuracy | 159, 169, 221 |
| abstract_inverted_index.contrast | 141 |
| abstract_inverted_index.datasets | 176 |
| abstract_inverted_index.detailed | 129 |
| abstract_inverted_index.driving. | 13 |
| abstract_inverted_index.features | 75, 155 |
| abstract_inverted_index.function | 143 |
| abstract_inverted_index.improved | 56 |
| abstract_inverted_index.increase | 146 |
| abstract_inverted_index.learning | 19 |
| abstract_inverted_index.original | 192 |
| abstract_inverted_index.problem, | 52 |
| abstract_inverted_index.proposed | 172 |
| abstract_inverted_index.CIFAR-10, | 181 |
| abstract_inverted_index.attention | 26, 113 |
| abstract_inverted_index.automatic | 12 |
| abstract_inverted_index.increased | 198 |
| abstract_inverted_index.real-time | 214 |
| abstract_inverted_index.represent | 29 |
| abstract_inverted_index.vehicles, | 108 |
| abstract_inverted_index.(IND-ViT). | 62 |
| abstract_inverted_index.difference | 152 |
| abstract_inverted_index.mechanisms | 27 |
| abstract_inverted_index.similarity | 105 |
| abstract_inverted_index.structures | 23 |
| abstract_inverted_index.technology | 2 |
| abstract_inverted_index.Caltech-101 | 185 |
| abstract_inverted_index.Flower-102, | 183 |
| abstract_inverted_index.consistency | 149 |
| abstract_inverted_index.development | 16 |
| abstract_inverted_index.discernible | 120 |
| abstract_inverted_index.foundations | 7 |
| abstract_inverted_index.information | 31, 45, 87 |
| abstract_inverted_index.inter-class | 151 |
| abstract_inverted_index.intra-class | 148 |
| abstract_inverted_index.requirement | 215 |
| abstract_inverted_index.technology, | 20 |
| abstract_inverted_index.transformer | 22, 58, 194 |
| abstract_inverted_index.Experimental | 164 |
| abstract_inverted_index.effectively. | 34 |
| abstract_inverted_index.misdetection | 100 |
| abstract_inverted_index.recognition. | 163 |
| abstract_inverted_index.segmentation | 78 |
| abstract_inverted_index.transformer. | 91 |
| abstract_inverted_index.BIT-Vehicles, | 180 |
| abstract_inverted_index.Respectively, | 196 |
| abstract_inverted_index.Specifically, | 63 |
| abstract_inverted_index.segmentation, | 40 |
| abstract_inverted_index.classification | 1, 60, 154, 162, 179 |
| abstract_inverted_index.representation | 131 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5046985719 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I70908550 |
| citation_normalized_percentile.value | 0.89984906 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |