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Road weather conditions are closed-related to the transportation safety and traffic capacity. With the development of road surveillance systems, weather conditions could be recognized from video. However, it is hard to be detected by machine. To address it, a deeply supervised convolution neural network (DS-CNN) is designed and trained on a self-established dataset. The traffic image dataset includes five groups labeled with “sunny”, “overcast”, “rainy”, “snowy” and “foggy”. Each group has manually labeled and selected more than 2500 images. The DS-CNN, can achieve the precision rate of 0.9681 and the recall rate of 0.9681. This practical weather detection method has been built in the surveillance system covers five freeways. The experimental result used in practice shows much worse detection results at first with disturbance of difference scenarios, worn camera, transmission failure and so on. With further improvement of DS-CNN, we found that it was much more effective than hand-crafted features in this task, and a deeper neural architecture could derive more powerful features. Moreover, the results show that dense weather information has more details in a small scale. In order to fast report the regional weather detection result, a designed visualization method is also proposed in spatiotemporal dimension to fuse with currently-used system. The high accuracy and fast detection speed with friendly visualization would lead to more precise traffic management and prompt the road weather traffic control to more intelligence levels.

The current environmental conditions should be monitored during autonomous driving since the different weather conditions can have a different impact on implemented sensor system or on the efficiency of the implemented control system. In this paper, the classification of weather conditions in the vehicle environment is based on images captured by a front-view camera, which are further processed by the simple Convolutional Neural Network (CNN). For model development purposes, training and validation data sets were created from two sources: the BDD100K database and by extracting frames from the collected video sequences. The solution implements an additional mechanism to filter out false predictions based on a circular buffer. The proposed solution achieves the F1 measure of 98.3% for the entire test video frames data set, where it achieves the best results in snowy weather detection (Precision of 100%, F1 of 100.00%) and the worst in foggy weather detection (Precision of 97.25%, F1 of 98.00%).