Wave interference network with a wave function for traffic sign recognition

Qiang Weng; Dewang Chen; Yuandong Chen; Wendi Zhao; Lin Jiao; Qiang Weng; Dewang Chen; Yuandong Chen; Wendi Zhao; Lin Jiao

doi:10.3934/mbe.2023851

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 11: 19254-19269. doi: 10.3934/mbe.2023851

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Wave interference network with a wave function for traffic sign recognition

School of Transportation, Fujian University of Technology, Fuzhou 350118, China

Academic Editor: Shangce Gao

Received: 04 August 2023 Revised: 19 September 2023 Accepted: 26 September 2023 Published: 16 October 2023

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.
- traffic sign recognition,
- wave function,
- deep neural networks,
- image classifier
Citation: Qiang Weng, Dewang Chen, Yuandong Chen, Wendi Zhao, Lin Jiao. Wave interference network with a wave function for traffic sign recognition[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19254-19269. doi: 10.3934/mbe.2023851

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Abstract

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.

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