Fine-grained vehicle type classification using lightweight convolutional neural network with feature optimization and joint learning strategy

• Published in: Multimedia tools and applications Vol. 80; no. 20; pp. 30803 - 30816
• Main Authors: Sun, Wei, Zhang, Guoce, Zhang, Xiaorui, Zhang, Xu, Ge, Nannan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2021; Springer Nature B.V
• Subjects: Accuracy; Artificial neural networks; Classification; Complexity; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Learning; Lightweight; Mathematical models; Multimedia Information Systems; Neural networks; Optimization; Parameters; Special Purpose and Application-Based Systems; Transport buildings, stations and terminals; Fine-grained vehicle type classification; Contrastive-center loss; Lightweight; Feature optimization

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 d...