Fine-grained Vehicle Recognition Using Lightweight Convolutional Neural Network with Combined Learning Strategy

Abstract

Fine-grained vehicle recognition plays an important part in applications, such as urban traffic management, public security, and criminal investigation. It has great challenges due to the subtle differences among numerous subcategories. In this paper, a fine-grained vehicle recognition method using lightweight convolutional neural network with combined learning strategy is proposed. Firstly, a lightweight Convolutional Neural Network (LWCNN) is designed specially for the fine-grained vehicle recognition task. Then, a combined training strategy, including pre-training, fine-tuning training and transfer training, is proposed to optimize the LWCNN parameters. In the pre-training phase, ILSVRC-2012 dataset is adopted to train the VGG16-Net, generating an initial model. Then, in the fine-tuning phase, the vehicle dataset is used for fine-tuning the pre-trained model to avoid learning parameters from scratch. Finally, in the transfer training phase, appropriate initialization parameters of LWCNN are obtained through the analysis of the fine-tuned network parameters. LWCNN is then trained using the vehicle dataset to obtain the highly accurate and robust classification model. Compared with the state-of-the-art methods, the proposed method can effectively decrease the computational complexity while maintaining the recognition performance.