Severe weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. A new simplified model named ResNet15 is proposed based on the residual network ResNet50 in this paper. The convolutional layers of ResNet15 are utilized to extract weather characteristics, and then the characteristics extracted at the previous layer are shortcut to the next layer through four groups of residual modules. Finally, the weather images are classified and recognized through the fully connected layer and Softmax classifier. In addition, we build a medium-scale dataset of weather images on traffic road, called “WeatherDataset-4,” which consists of 4 categories and contains 4983 weather images covering most of the severe weather. In this paper, ResNet15 is used to train and test on the “WeatherDataset-4,” and desirable recognition results are obtained. The evaluation of a large number of experiments demonstrates that the proposed ResNet15 is superior to traditional network models such as ResNet50 in recognition accuracy, recognition speed, and model size.

中文翻译：

---

ResNet15：深度卷积神经网络在交通道路上的天气识别

恶劣的天气条件将对城市交通产生重大影响。天气状况自动识别在交通状况预警，汽车辅助驾驶，智能交通系统等方面具有重要的应用价值。随着深度学习的飞速发展，深度卷积神经网络（CNN）被用于识别交通道路上的天气状况。本文基于残网ResNet50，提出了一种新的简化模型ResNet15。利用ResNet15的卷积层提取天气特征，然后通过四组残差模块在上一层提取的特征通向下一层。最后，通过完全连接的层和Softmax分类器对天气图像进行分类和识别。此外，我们在交通道路上建立了一个中等规模的天气图像数据集，称为“ WeatherDataset-4”，它分为4类，包含4983个覆盖大部分恶劣天气的天气图像。本文使用ResNet15对“ WeatherDataset-4”进行训练和测试，获得了令人满意的识别结果。对大量实验的评估表明，所提出的ResNet15在识别精度，识别速度和模型大小方面优于传统网络模型，例如ResNet50。