关键词: 细粒度车辆识别, 车辆多属性, 多任务学习, 卷积神经网络, 度量学习, 车辆多属性数据集

Abstract: Fine-grained vehicle identification is challenging, especially when the two vehicles differ in appearance and subtleness. However, the general neural network model ignores the connection between the additional attributes. This paper proposes a convolution neural network based on improved triplet loss training for vehicle multi-attribute learning, which is used to implement fine-grained vehicle identification. Specifically, by changing the structure of the traditional neural network, the vehicle identification problem is transformed into a multi-attribute learning problem. In this paper, the triplet loss function is improved to train the network to achieve fine-grained vehicle identification. At the same time, it creates a multi-attribute vehicle data set and completes the training work. The results show the potential of the method.

Key words: fine-grained vehicle recognition, vehicle multi-attribute, multi-task learning, convolution neural network, metric learning, vehicle multi-attribute data