针对深层卷积神经网络模型参数量大、对硬件设备要求高而难以部署于边缘端的问题，本文结合高速公路监控图像这一应用场景，对基于轻量化神经网络的天气识别算法进行研究。首先对经典的轻量化神经网络模型MobileNet进行理论分析，由参数量和计算次数的角度分析其深度可分离卷积与标准卷积操作的不同。同时，收集并标注基于公路监控图像的天气识别数据集。在此基础上，搭建并训练包含多个轻量化神经网络在内的模型进行对比实验，实验结果验证了MobileNet在识别精度、速度以及模型参数量等指标上的优势。此外，本文通过可视化算法t-SNE从类别响应分析和特征分布两个方面探讨MobileNet的特征表征能力以及特征的类间可分性和类内可聚性，其结果进一步支撑了上述分析。

For the problem that deep convolutional neural network models are difficult to deploy at the edge due to their large number of parameters and high hardware device requirements, this paper researched on the weather re-cognition algorithm based on light-weight neural network with the application scenario of highway surveillance images. The light-weight neural network model MobileNet was first analyzed theoretically, and the difference between the deeply separable convolution operation and the standard convolution operation was analyzed in terms of the number of parameters and the number of computations. At the same time, a weather recognition dataset based on highway surveillance images was collected and labeled. Based on this, models including several light-weight neural networks were built and trained for comparison experiments, and the experimental results verified the advantages of MobileNet in terms of recognition accuracy, speed and number of model parameters. In addition, this paper explored the feature representation of MobileNet as well as the inter-class separability and intra-class clustering of features by the visualization algorithm t-SNE in terms of both class activation analysis and feature distribution, and the results further supported the above analysis.