Wei Wei, Zhang Lili, Yang Kang, Li Jing, Cui Ning, Han Yucheng, Zhang Ning, Yang Xudong, Tan Hongxin, Wang Kai
Beijing Institute of Petrochemical Technology, Beijing, 102617, China.
Science and Technology on Complex Aviation Systems Simulation Laboratory, Beijing, 100076, China.
Heliyon. 2024 Feb 15;10(4):e26182. doi: 10.1016/j.heliyon.2024.e26182. eCollection 2024 Feb 29.
Traffic sign recognition is an important part of intelligent transportation system. It uses computer vision and traffic sign recognition technology to detect and recognize traffic signs on the road automatically. In this paper, we propose a lightweight model for traffic sign recognition based on convolutional neural networks called ConvNeSe. Firstly, the feature extraction module of the model is constructed using the Depthwise Separable Convolution and Inverted Residuals structures. The model extracts multi-scale features with strong representation ability by optimizing the structure of convolutional neural networks and fusing of features. Then, the model introduces Squeeze and Excitation Block (SE Block) to improve the attention to important features, which can capture key information of traffic sign images. Finally, the accuracy of the model in the German Traffic Sign Recognition Benchmark Database (GTSRB) is 99.85%. At the same time, the model has good robustness according to the results of ablation experiments.
交通标志识别是智能交通系统的重要组成部分。它利用计算机视觉和交通标志识别技术自动检测和识别道路上的交通标志。在本文中，我们提出了一种基于卷积神经网络的轻量级交通标志识别模型，称为ConvNeSe。首先，使用深度可分离卷积和倒置残差结构构建模型的特征提取模块。该模型通过优化卷积神经网络的结构和特征融合来提取具有强大表示能力的多尺度特征。然后，模型引入挤压与激励模块（SE模块）以提高对重要特征的关注度，从而能够捕捉交通标志图像的关键信息。最后，该模型在德国交通标志识别基准数据库（GTSRB）中的准确率为99.85%。同时，根据消融实验的结果，该模型具有良好的鲁棒性。
