With the development of cities, the rapid growth of vehicle ownership has given rise to traffic violations and traffic safety problems resulting in casualties. Therefore, with the rise of intelligent transportation in smart cities, intelligent traffic video monitoring systems have attracted considerable attention. The industry and academia have begun to consider the problem of increasing the intelligent functions of video monitoring systems. The intelligent functions of current intelligent traffic video monitoring systems focus on object detection and tracking, and abnormal situation alarms, for example. As the core functions of intelligent traffic video monitoring systems involve vehicle detection and classification of fine-grained problems, research in this area is very difficult. Moreover, no good, substantial products are available, especially with the fine-grained vehicle classification function, and no practical research has addressed this issue. In this paper, we propose an approach based on the fusion of convolutional neural networks (CNNs) to solve the problem of vehicle detection and fine-grained classification. In the fine-grained vehicle classification problem, the differences in a class are greater than the differences between classes. As a result, the classification accuracy is not sufficiently high to achieve efficient fine-grained vehicle classification.