A Practical Weather Detection Method Built in the Surveillance System Currently Used to Monitor the Large-Scale Freeway in China

Abstract

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. INDEX TERMS Weather conditions detection, road surveillance system, deep convolution neural network.

Classification of Rainfall Intensity and Cloud Type from Dash Cam Images Using Feature Removal by Masking

“…However, since users are limited regarding the time at which and the place where they can report data, it is necessary to obtain data from other sources. [5] aimed to improve the temporal and spatial resolution by monitoring weather conditions from road surveillance camera images. A road weather dataset with sunny, cloudy, rainy, snowy, and foggy weather labels was constructed using recorded images from a road surveillance system in China.…”

“…However, the appearance of the sky during rainfall events with different intensities is less distinct than that of the sky in other weather conditions (clear, cloudy, and rainy). In addition, consideration was given to learning the features of the road separately, given the relationship between weather and road conditions [5,10]. Therefore, masking is applied to non-road areas to learn only road features.…”