Recognizing the current weather conditions from a single image is of great theoretical significance. It also has potential practical value for daily life and traffic scheduling. To achieve that, typical weather recognition methods focus on learning a general weather description, e.g., sunny, cloudy, foggy, rainy and snowy etc, for the overall weather condition. However, it is far away from being sufficient for many tasks especially traffic management and control. To solve this key problem, this paper proposes a Global-Similarity Local-Salience Network (abbreviated as GSLSNet) for traffic weather recognition. Specifically, a simple but effective Global-Similarity Module (GSM) is proposed to recognize the overall weather condition and a Local-Salience Module (LSM) is presented to restrict the network to focus on road weather details. Besides, this paper also provides a new traffic weather dataset, named TWData, which is the first fine categorized dataset especially for highway weather recognition. Experimental results compared with state-of-the-art methods on both public datasets and TWData demonstrate the superiority of the proposed GSLSNet.

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It is a very effective studying method to cultivate the students' interest, but the key is how to make the students interested in the content of the learning. This paper researches a teaching method of the reversal mode. The method is to let a student to play the role of the teacher, however, the teacher is to be a student. It can make the students get knowledge very well by themselves and give full play to their potential and the subjective initiative. Compared with the previous conventional teaching, the students master the knowledge points more clearly. This method is real and effective, we get good results through a semester of teaching.