在实际交通环境中,所采集到的交通标志图像质量往往受到运动模糊、背景干扰、天气条件以及拍摄视角等因素的影响,这对交通标志自动识别的准确性、实时性和稳健性提出了很大的挑战。为此提出了改进深度卷积神经网络AlexNet的分类识别算法模型,该模型在传统AlexNet模型基础上,以真实场景中拍摄的交通标志图像数据集GTSRB为研究对象,将所有卷积层的卷积核修改为3×3大小,为了预防和减少过拟合的出现在两个全连接层后加入dropout层,并且为了提高交通标志识别精度,在网络模型第5层后增加两层卷积层。实验结果表明,改进后AlexNet模型在交通标志识别方面具有一定的先进性和稳健性。

In the actual traffic environment, the quality of the collected traffic signs is often influenced by the factors such as motion blur, background interference, weather conditions and shooting angles and so on, which poses a great challenge to the accuracy, real-time and robustness of traffic sign automatic identification. Owing to this, a classification recognition algorithm model of improved deep convolution neural network AlexNet is proposed. On the basis of the traditional AlexNet model, this model takes the traffic sign image data set GTSRB taken in the real scene as the research object, modifies the convolution kernels of all coiling layers to 3×3, in order to prevent and reduce the occurrence of over fitting, the dropout layer is added after two fully connected layers. In order to improve the accuracy of traffic sign recognition, two convolution layers are added after the fifth layer of the network model. The experimental results show that the improved AlexNet model is advanced and robust in traffic sign recognition.