关键词: 车辆精细型号识别, 卷积神经网络, 多尺度特征融合, 深度学习

Abstract: The fine-grained vehicle model is one of the main clues for vehicle recognition and an important part of the intelligent transportation system. A multi-scale feature fusion Convolutional Neural Network（CNN） model is proposed, which aims to solve the recognition difficulty caused by the wide variety of vehicle models and the complex and changeable environment. This method is based on the traditional CNN. It can accomplish the fine-grained recognition of the vehicle model by extracting and fusing vehicle features from the bottom layer and the top layer of the network. Compared with other recognition methods based on CNN, the proposed method can significantly reduce the size of network parameters while improving the classification accuracy. Using the public dataset CompCars, the experimental results show that the method reaches a recognition accuracy of 98.43%, and the model parameter size is only 3.93 MB. The average recognition time for each image is 0.83 ms.

Key words: fine-grained recognition of vehicle model, Convolutional Neural Network（CNN）, multi-scale feature fusion, deep learning