ORIGINAL ARTICLE
DFT: A deep feature-based semi-supervised collaborative training for vehicle recognition in smart cities
Yichuan Zhang,
Yadi Liu,
Guangming Yang,
Jie Song,
Yichuan Zhang
School of Software, Northeastern University, Shenyang, China
Search for more papers by this authorYadi Liu
School of Software, Northeastern University, Shenyang, China
Search for more papers by this authorGuangming Yang
School of Software, Northeastern University, Shenyang, China
Search for more papers by this authorCorresponding Author
Jie Song
School of Software, Northeastern University, Shenyang, China
Correspondence
Jie Song, School of Software, Northeastern University, Shenyang, Liaoning, 110169, China.
Email: [email protected]
Search for more papers by this authorYichuan Zhang,
Yadi Liu,
Guangming Yang,
Jie Song,
Yichuan Zhang
School of Software, Northeastern University, Shenyang, China
Search for more papers by this authorYadi Liu
School of Software, Northeastern University, Shenyang, China
Search for more papers by this authorGuangming Yang
School of Software, Northeastern University, Shenyang, China
Search for more papers by this authorCorresponding Author
Jie Song
School of Software, Northeastern University, Shenyang, China
Correspondence
Jie Song, School of Software, Northeastern University, Shenyang, Liaoning, 110169, China.
Email: [email protected]
Search for more papers by this authorFirst published: 10 December 2021
Correction added on 23 December 2021, after first online publication: The title has been corrected in this version.
Abstract
Intelligent transport systems (ITS) is a popular field of research in smart city. Image-based vehicle recognition is one of the most promising new techniques. The traditional appearance-based method has some limitations in feature view. The machine learning algorithm can satisfy multiple feature views, but it mainly adopts an improved supervised learning method, which needs manual annotation and has low efficiency. The semi-supervised collaborative training method is widely used in the image recognition process to improve machine learning algorithms' accuracy and generalization performance. This paper proposes DFT (deep feature-based training) method for vehicle recognition in smart cities. DFT is also a semi-supervised collaborative training method on basis of two base learners. DFT adjusts data pre-processing and training process, optimizes the constructing a disagreement encoding network, and expands the recognition disagreement of pseudo-labelled samples-based training set. Compared with the typical collaborative training methods, DFT greatly accelerates the model's training process by reducing the convergence time, and improves the efficiency of vehicle recognition, while remaining the recognition accuracy unchanged.
CONFLICT OF INTEREST
The authors have no conflicts of interest to declare.
Open Research
DATA AVAILABILITY STATEMENT
Data is openly available in a public repository that issues datasets with DOIs.
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