基于深度学习的公路能见度分类及应用

Classification and application of highway visibility based on deep learning

以VGG16为基准模型,融合批归一化处理、全局平均池化和联合损失函数,提出了一种基于卷积神经网络的高速公路雾天能见度等级分类方法。实验结果表明,改进后的神经网络模型的平均识别正确率达83.9%,相较于其他几种模型具有较高的正确率和较好的收敛性。将模型封装入业务系统后进行业务化检验,其平均识别正确率可达84.9%,且白天识别效果要优于夜间。通过系统监测到2019年4月4日京沪高速发生了一次团雾动态生消过程。该次团雾过程具有移动快、范围小、生存时间短的特征。系统的应用能够为交通管理部门应对团雾发生时的智能管控和决策调度提供技术支持。

Taking VGG16as the benchmark model,integrating batch normalization,global average pooling and joint loss function,this paper proposed a highway fog visibility classification method based on the convolutional neural network.The experimental results show that the average recognition accuracy of the improved neural network model is83.9%,which has higher accuracy and better convergence than other models.After the model is encapsulated into the business system for operational verification,the average recognition accuracy can reach84.9%,and the recognition performance in the daytime is better than that at night.A dynamic generation and elimination process of agglomerate fog in Beijing-Shanghai Expressway on April4,2019was monitored by the business system.The agglomerate fog process has the characteristics of fast movement,small range and short survival time.The application of the system can provide technical support for the traffic management department to deal with the intelligent management and control and decision-making scheduling when the fog occurs.