Wave interference network with a wave function for traffic sign recognition
In this paper, we successfully combine convolution with a wave function to build an effective and efficient classifier for traffic signs, named the wave interference network (WiNet). In the WiNet, the feature map extracted by the convolutional filters is refined into many entities from an input imag...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2023-10-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023851?viewType=HTML |
| _version_ | 1827762428500246528 |
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| author | Qiang Weng Dewang Chen Yuandong Chen Wendi Zhao Lin Jiao |
| author_facet | Qiang Weng Dewang Chen Yuandong Chen Wendi Zhao Lin Jiao |
| author_sort | Qiang Weng |
| collection | DOAJ |
| description | In this paper, we successfully combine convolution with a wave function to build an effective and efficient classifier for traffic signs, named the wave interference network (WiNet). In the WiNet, the feature map extracted by the convolutional filters is refined into many entities from an input image. Each entity is represented as a wave. We utilize Euler's formula to unfold the wave function. Based on the wave-like information representation, the model modulates the relationship between the entities and the fixed weights of convolution adaptively. Experiment results on the Chinese Traffic Sign Recognition Database (CTSRD) and the German Traffic Sign Recognition Benchmark (GTSRB) demonstrate that the performance of the presented model is better than some other models, such as ResMLP, ResNet50, PVT and ViT in the following aspects: 1) WiNet obtains the best accuracy rate with 99.80% on the CTSRD and recognizes all images exactly on the GTSRB; 2) WiNet gains better robustness on the dataset with different noises compared with other models; 3) WiNet has a good generalization on different datasets. |
| first_indexed | 2024-03-11T10:29:34Z |
| format | Article |
| id | doaj.art-433dafa4c27d4eb5b3f1255764af94ae |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-11T10:29:34Z |
| publishDate | 2023-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-433dafa4c27d4eb5b3f1255764af94ae2023-11-15T01:09:58ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-10-012011192541926910.3934/mbe.2023851Wave interference network with a wave function for traffic sign recognitionQiang Weng0Dewang Chen 1Yuandong Chen2Wendi Zhao3Lin Jiao4School of Transportation, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Transportation, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Transportation, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Transportation, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Transportation, Fujian University of Technology, Fuzhou 350118, ChinaIn this paper, we successfully combine convolution with a wave function to build an effective and efficient classifier for traffic signs, named the wave interference network (WiNet). In the WiNet, the feature map extracted by the convolutional filters is refined into many entities from an input image. Each entity is represented as a wave. We utilize Euler's formula to unfold the wave function. Based on the wave-like information representation, the model modulates the relationship between the entities and the fixed weights of convolution adaptively. Experiment results on the Chinese Traffic Sign Recognition Database (CTSRD) and the German Traffic Sign Recognition Benchmark (GTSRB) demonstrate that the performance of the presented model is better than some other models, such as ResMLP, ResNet50, PVT and ViT in the following aspects: 1) WiNet obtains the best accuracy rate with 99.80% on the CTSRD and recognizes all images exactly on the GTSRB; 2) WiNet gains better robustness on the dataset with different noises compared with other models; 3) WiNet has a good generalization on different datasets.https://www.aimspress.com/article/doi/10.3934/mbe.2023851?viewType=HTMLtraffic sign recognitionwave functiondeep neural networksimage classifier |
| spellingShingle | Qiang Weng Dewang Chen Yuandong Chen Wendi Zhao Lin Jiao Wave interference network with a wave function for traffic sign recognition Mathematical Biosciences and Engineering traffic sign recognition wave function deep neural networks image classifier |
| title | Wave interference network with a wave function for traffic sign recognition |
| title_full | Wave interference network with a wave function for traffic sign recognition |
| title_fullStr | Wave interference network with a wave function for traffic sign recognition |
| title_full_unstemmed | Wave interference network with a wave function for traffic sign recognition |
| title_short | Wave interference network with a wave function for traffic sign recognition |
| title_sort | wave interference network with a wave function for traffic sign recognition |
| topic | traffic sign recognition wave function deep neural networks image classifier |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023851?viewType=HTML |
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