MHS-VIT: Mamba hybrid self-attention vision transformers for traffic image detection
With the rapid development of intelligent transportation systems, especially in traffic image detection tasks, the introduction of the transformer architecture greatly promotes the improvement of model performance. However, traditional transformer models have high computational costs during training...
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| Vydané v: | PloS one Ročník 20; číslo 6; s. e0325962 |
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| Médium: | Journal Article |
| Jazyk: | English |
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United States
Public Library of Science
30.06.2025
Public Library of Science (PLoS) |
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| Abstract | With the rapid development of intelligent transportation systems, especially in traffic image detection tasks, the introduction of the transformer architecture greatly promotes the improvement of model performance. However, traditional transformer models have high computational costs during training and deployment due to the quadratic complexity of their self-attention mechanism, which limits their application in resource-constrained environments. To overcome this limitation, this paper proposes a novel hybrid architecture, Mamba Hybrid Self-Attention Vision Transformers (MHS-VIT), which combines the advantages of Mamba state-space model (SSM) and transformer to improve the modeling efficiency and performance of visual tasks and to enhance the modeling efficiency and accuracy of the model in processing traffic images. Mamba, as a linear time complexity SSM, can effectively reduce the computational burden without sacrificing performance. The self-attention mechanism of the transformer is good at capturing long-distance spatial dependencies in images, which is crucial for understanding complex traffic scenes. Experimental results showed that MHS-VIT exhibited excellent performances in traffic image detection tasks. Whether it is vehicle detection, pedestrian detection, or traffic sign recognition tasks, this model could accurately and quickly identify target objects. Compared with backbone networks of the same scale, MHS-VIT achieved significant improvements in accuracy and model parameter quantity. |
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| AbstractList | With the rapid development of intelligent transportation systems, especially in traffic image detection tasks, the introduction of the transformer architecture greatly promotes the improvement of model performance. However, traditional transformer models have high computational costs during training and deployment due to the quadratic complexity of their self-attention mechanism, which limits their application in resource-constrained environments. To overcome this limitation, this paper proposes a novel hybrid architecture, Mamba Hybrid Self-Attention Vision Transformers (MHS-VIT), which combines the advantages of Mamba state-space model (SSM) and transformer to improve the modeling efficiency and performance of visual tasks and to enhance the modeling efficiency and accuracy of the model in processing traffic images. Mamba, as a linear time complexity SSM, can effectively reduce the computational burden without sacrificing performance. The self-attention mechanism of the transformer is good at capturing long-distance spatial dependencies in images, which is crucial for understanding complex traffic scenes. Experimental results showed that MHS-VIT exhibited excellent performances in traffic image detection tasks. Whether it is vehicle detection, pedestrian detection, or traffic sign recognition tasks, this model could accurately and quickly identify target objects. Compared with backbone networks of the same scale, MHS-VIT achieved significant improvements in accuracy and model parameter quantity. With the rapid development of intelligent transportation systems, especially in traffic image detection tasks, the introduction of the transformer architecture greatly promotes the improvement of model performance. However, traditional transformer models have high computational costs during training and deployment due to the quadratic complexity of their self-attention mechanism, which limits their application in resource-constrained environments. To overcome this limitation, this paper proposes a novel hybrid architecture, Mamba Hybrid Self-Attention Vision Transformers (MHS-VIT), which combines the advantages of Mamba state-space model (SSM) and transformer to improve the modeling efficiency and performance of visual tasks and to enhance the modeling efficiency and accuracy of the model in processing traffic images. Mamba, as a linear time complexity SSM, can effectively reduce the computational burden without sacrificing performance. The self-attention mechanism of the transformer is good at capturing long-distance spatial dependencies in images, which is crucial for understanding complex traffic scenes. Experimental results showed that MHS-VIT exhibited excellent performances in traffic image detection tasks. Whether it is vehicle detection, pedestrian detection, or traffic sign recognition tasks, this model could accurately and quickly identify target objects. Compared with backbone networks of the same scale, MHS-VIT achieved significant improvements in accuracy and model parameter quantity.With the rapid development of intelligent transportation systems, especially in traffic image detection tasks, the introduction of the transformer architecture greatly promotes the improvement of model performance. However, traditional transformer models have high computational costs during training and deployment due to the quadratic complexity of their self-attention mechanism, which limits their application in resource-constrained environments. To overcome this limitation, this paper proposes a novel hybrid architecture, Mamba Hybrid Self-Attention Vision Transformers (MHS-VIT), which combines the advantages of Mamba state-space model (SSM) and transformer to improve the modeling efficiency and performance of visual tasks and to enhance the modeling efficiency and accuracy of the model in processing traffic images. Mamba, as a linear time complexity SSM, can effectively reduce the computational burden without sacrificing performance. The self-attention mechanism of the transformer is good at capturing long-distance spatial dependencies in images, which is crucial for understanding complex traffic scenes. Experimental results showed that MHS-VIT exhibited excellent performances in traffic image detection tasks. Whether it is vehicle detection, pedestrian detection, or traffic sign recognition tasks, this model could accurately and quickly identify target objects. Compared with backbone networks of the same scale, MHS-VIT achieved significant improvements in accuracy and model parameter quantity. |
| Audience | Academic |
| Author | Zhang, Xude Ou, Weihua Wu, Xiaoping Zhang, Changzhen |
| AuthorAffiliation | 3 School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, China 1 Engineering Research Center of Micro-Nano and Intelligent Manufacturing, Ministry of Education, Kaili University, Kaili, Guizhou, China Prince Mohammad Bin Fahd University, SAUDI ARABIA 2 College of Microelectronics and Artificial Intelligence, Kaili University, Kaili, Guizhou, China |
| AuthorAffiliation_xml | – name: 2 College of Microelectronics and Artificial Intelligence, Kaili University, Kaili, Guizhou, China – name: 1 Engineering Research Center of Micro-Nano and Intelligent Manufacturing, Ministry of Education, Kaili University, Kaili, Guizhou, China – name: Prince Mohammad Bin Fahd University, SAUDI ARABIA – name: 3 School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, China |
| Author_xml | – sequence: 1 givenname: Xude orcidid: 0009-0001-6861-1196 surname: Zhang fullname: Zhang, Xude – sequence: 2 givenname: Weihua surname: Ou fullname: Ou, Weihua – sequence: 3 givenname: Xiaoping surname: Wu fullname: Wu, Xiaoping – sequence: 4 givenname: Changzhen surname: Zhang fullname: Zhang, Changzhen |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40587465$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s11042-023-17456-6 10.1016/j.engappai.2023.106686 10.1109/ICCV48922.2021.00986 10.1109/ICCV.2015.169 10.1109/CVPR52733.2024.01683 10.1109/CVPR52729.2023.00721 10.1007/978-3-031-72973-7_6 10.1109/TPAMI.2022.3223955 10.1109/CVPR.2017.243 10.1109/CVPR.2016.91 10.1109/CVPR.2017.690 10.1109/CVPR52733.2024.01605 10.1109/CVPR.2016.90 10.1109/CVPR52688.2022.01897 10.1109/TPAMI.2016.2577031 10.1109/CVPR.2014.81 10.1007/978-3-030-58452-8_13 |
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| SubjectTerms | Accuracy Algorithms Attention Biology and Life Sciences Complexity Computer and Information Sciences Computer applications Computer vision Computing costs Deep learning Engineering and Technology Humans Image detection Image processing Image Processing, Computer-Assisted - methods Intelligent transportation systems Localization Machine vision Methods Models, Theoretical Neural networks Object recognition Physical Sciences Research and Analysis Methods Social Sciences Spatial dependencies State space models Technology application Time series Traffic Traffic engineering Traffic signs Vision Visual tasks |
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| Title | MHS-VIT: Mamba hybrid self-attention vision transformers for traffic image detection |
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| Volume | 20 |
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