基于改进残差网络的交通标志识别算法

摘要/Abstract

摘要： 针对模型在下采样过程中不断损失图像的高层次信息，从而导致特征提取不足的问题，本文对ResNet网络结构进行改进，提出基于多尺度特征与注意力机制的交通标志识别方法。首先，通过特征融合的方式将模型各个层次的多尺度特征进行融合，丰富特征语义信息，增强网络的特征提取能力。然后，通过注意力机制强化不同通道特征，提升特征整体的表达能力。结合这2种方法可提升模型的交通标志识别准确率。在GTSRB和BelgiumTS交通标志数据集上的实验结果表明，所提出方法的准确率分别达到99.31%和98.96%，优于前沿的交通标志识别算法。

关键词: 深度学习, 交通标志识别, 卷积神经网络, 多尺度特征融合, 通道注意力机制

Abstract: For the problems of high-level information loss and insufficient feature extraction in sampling in network structure, the ResNet network structure is improved and a traffic sign recognition method based on multi-scale features and attention mechanism is put forward in this paper. Firstly, multi-scale features are used to fuse different levels of feature information to enrich feature semantic information and enhance the ability of feature extraction. Then, the features of different channels are strengthened through the attention mechanism to improve the overall presence of traffic signs for achieving more accurate traffic sign recognition. The experimental results on GTSRB and BelgiumTS traffic sign datasets show that the accuracies with the proposed methods reach 99.31% and 98.96% respectively, which achieves better results in traffic sign recognition.

Key words: deep learning, traffic sign recognition, convolutional neural network, multi-scale feature fusion, channel attention mechanism

梁正友, 耿经邦, 孙宇. 基于改进残差网络的交通标志识别算法[J]. 计算机与现代化, 2022, 0(04): 52-57.

LIANG Zheng-you, GENG Jing-bang, SUN Yu. Traffic Sign Recognition Algorithm Based on Improved Residual Network[J]. Computer and Modernization, 2022, 0(04): 52-57.

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