The Community for Technology Leaders
2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) (2018)
Xi'an
Sept. 13, 2018 to Sept. 16, 2018
ISBN: 978-1-5386-5322-7
pp: 1-5
Qiang Zhang , Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System
Li Zhuo , Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System
Shiyu Zhang , Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System
Jiafeng Li , Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System
Hui Zhang , Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System
Xiaoguang Li , Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System
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.
INDEX TERMS
computer vision, feature extraction, feedforward neural nets, image classification, image recognition, learning (artificial intelligence), object detection, object recognition, object tracking
CITATION

Q. Zhang, L. Zhuo, S. Zhang, J. Li, H. Zhang and X. Li, "Fine-grained Vehicle Recognition Using Lightweight Convolutional Neural Network with Combined Learning Strategy," 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM)(BIGMM), Xi'an, 2018, pp. 1-5.
doi:10.1109/BigMM.2018.8499085
153 ms
(Ver 3.3 (11022016))