一种改进的深度学习的道路交通标识识别算法

针对复杂的环境，结合图像预处理与深度学习神经网络，提出了一种道路交通标识识别算法。该方法不仅利用图像分割技术，而且利用卷积神经网络模型对道路交通标识进行了更准确的识别。首先，通过调节光照影响、去除复杂背景、数据增强和归一化等批量预处理操作，形成一个完整的数据集；然后，结合squeeze-and-excitation思想和残差网络结构，充分训练出自己的卷积神经网络模型；最后，将优化的网络模型用于道路交通标识的识别。实验结果表明，该方法使训练时间缩短了12%左右，识别精度可达99.26%。

An improved deep learning algorithm for road traffic identification

This study proposes a road traffic identification algorithm based on image preprocessing and deep-learning neural networks for complex environments. The proposed method uses not only the image segmentation technology but also the convolutional neural network model to more accurately identify the road traffic signs. First, a complete dataset is obtained via batch preprocessing operations, including illumination effect adjustment, complex background elimination, data enhancement, and normalization. Next, the convolutional neural network model is sufficiently trained based on the combination of the squeeze-and-excitation network and residual network structure concepts. Finally, the optimized network model is used to identify the road traffic signs. The experimental result shows that the proposed method reduces the training time by approximately 12% and that the recognition accuracy can reach 99.26%.