Fine-grained vehicle type classification using lightweight convolutional neural network with feature optimization and joint learning strategy - Sun, Wei; Zhang, Guoce; Zhang, Xiaorui; Zhang, Xu; Ge, Nannan (Multimedia tools and applications, 2021)
Vehicle type classification (VTC) plays an important role in today’s intelligent transportation. Previous VTC systems usually run on a monitoring center’s host machine due to the models’ complexity, which consume lots of computing resources and have poor real-time performance. If these systems are deployed to embedded terminals by making the model lightweight while ensuring accuracy, then the problem can be addressed. To this end, we propose a fine-grained VTC method using lightweight convolutional neural network with feature optimization and joint learning strategy. Firstly, a lightweight convolutional network with feature optimization (LWCNN-FO) is designed. We use depthwise separable convolution to reduce network parameters. Besides, the SENet module is added to obtain the important degree of each feature channel automatically through the sample-based self-learning, which can improve recognition accuracy with less network parameters growth. In addition, considering both between-class similarity and intra-class variance, this paper adopts the joint learning strategy combining softmax loss and contrastive-center loss to class vehicle types, thereby improving model’s fine-grained classification ability. We also build a dataset, called Car-159, consisting of 7998 pictures for 159 vehicle types, to evaluate our method. Compared with the state-of-the-art methods, experimental results show that our method can effectively decrease model’s complexity while maintaining accuracy.