基于双路神经网络融合模型的高速公路雾天检测

高速公路天气状况实时监察对于高速行车安全具备重要意义，然而气象检测只能对大范围区域的气象情况进行预报，不能满足高速行车各个路段气象情况实时检测的需求. 为此，提出一种基于双路神经网络融合模型的高速公路雾天检测算法. 该算法基于双路深度神经网络融合模型，提取雾天图像的可视深度图以及暗通道图像两种视觉特征，并利用深度神经网络进行建模，获得初步分类结果；然后，再利用均值融合层进行分数融合. 为了全面评测该算法的性能，构建了一个覆盖多个省份高速公路的视频监控雾天数据集（express way fog detection dataset，EWFD），该数据集能够全面涵盖国内高速公路的天气情况，并在该数据集上做了全面的分析对比实验. 实验结果显示，本文所提出的双路神经网络融合模型的雾天监测算法取得了93.7%的准确率，与国际前沿的检测分类算法101层残差网络（ResNet-101）相比，本文提出的算法准确率提高了10%以上. 

Two-Stream Neural Network Fusion Model for Highway Fog Detection

The real-time detection of weather conditions on highways has a significant impact on high-speed traffic safety. However, the weather forecast reports weather conditions over a wide range of areas only, which cannot meet the demand of real-time detection of weather conditions in various sections of high-speed traffic. Therefore, we present here a two-stream neural network fusion model for fog detection, which detect current weather condition for the surveillance area automatically. This model is based on a dual branches of deep neural networks, which integrates visual depth maps and dark-channel images for fog detection. These two modalities of features are discriminative in representing the pattern of fog and extracted from the surveillance video frame. The intermediate scores produced by the neural networks are fed into a mean fusion layer for the final prediction. To comprehensively evaluate the performance of our algorithm, we built an Express Way Fog Detection dataset （EWFD）, which covers highway scenes across multiple provinces of China. A variety of highway weather conditions are contained in the EWFD. We conducted a comprehensive analysis and comparison experiment on the EWFD dataset. The results of the experiment also demonstrate that the two-stream neural network fusion model proposed here achieved an accuracy of 93.7%, which is a more than 10% improvement compared to the state-of-the-art classification method ResNet-101.