Research on Online Classification of Road Vehicle Types Based on Electromagnetic Induction

Ye Qing, Liu Jianxiong, Liu Zheng, Chen Zhong, Li Liang


The vehicle classification correct rate of loop induction detector widely used on road is not high. The main reason is the classifier of fixed classification rules cannot cope with the changes of vehicle's complicated models and new vehicle's type classing. Based on the electromagnetic induction characteristic waveform of the vehicle passing through the loop,a new real-time discriminant method for vehicle classification was proposed. The principal component analysis method was used to extract the features. The adaptive resonance neural network algorithm was applied to cluster classification modes,these were dynamically divided into vehicle types then. For new vehicles of unknown vehicle type,new classification modes were added online by semi-supervised learning to adapt to the recognition of new vehicle type. The average correct rate of road real-time vehicle identification experiments of 7 models was 91.3%, and it was increased to 92.5% after adding new mode automatic recognition. In the comparative experiment with Alexnet multi-layer convolutional neural network algorithm, the correct rate of training set and test set were 99.5% and 87.1% respectively, which signified the existence of big differences. The experimental results verified the feasibility of the proposed method to solve the road vehicle identification problem of the change of vehicle mode.



Keywords:  vehicle type,  electromagnetic induction,  adaptive resonance neural network,  principal components

Full Text:



GAO J H,YANG Y M. Summarization of techniques and development of detection for road traffic vehicles[J]. Technology of Highway and Transport,2012(1):116—119.(In Chinese)

LIU W M,WANG Z R,ZHENG B T. Theory and method of expressway toll collection system [M]. Beijing:China Communications Press,2000:2—40.(In Chinese)

ZHANG Q,LI J F,ZHUO L. Review of vehicle recognition technology[J]. Journal of Beijing University of Technology,2018,44(3): 387—391.(In Chinese)

YANG Z,PUN -CHENG L S C. Vehicle detection in intelligent transportation systems and its applications under varying environments:a review[J]. Image and Vision Computing,2018,69:143— 154.

LIU H X,SUN J. Length-based vehicle classification using event- based loop detector data [J]. Transportation Research Part C, 2014,38:156—166.

LAMAS J J,CASTRO P M,DAPENA A,et al. Multi-loop inductive sensor model for vehicle traffic applications [J]. Sensors and Actuators A Physical,2017,263:580—592.

CAOG H. Survey of vehicle feature extraction methods [J]. China Water Transport(Theory Edition),2006,4(3):125—126. (In Chinese)

SUN L H,DOU W J. Overview of patent technology of vehicle detection technology [J]. China New Telecommunications,2015,17 (5):125—126.(In Chinese)

GAJDA J,SROKA R,STENCEL M. A vehicle classification based on inductive loop detectors [C]// Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Piscatway,NJ:IEEE,2001:460—464.

SROKA R. Data fusion methods based on fuzzy measures in vehicle classification process [C]// Proceedings of the 21st IEEE Instru mentation and Measurement Technology Conference. Piscataway, NJ:IEEE,2004:2234—2239.

GAJDA J,STENCEL M. A highly selective vehicle classification utilizing dual-loop inductive detector[J]. Metrology and Measurement Systems,2014,21(3):473—484.

KI Y -K,BAIK D -K. Vehicle classification algorithm for single loop detectors using neural networks [J]. IEEE Transactions on Vehicular Technology ,2006,55(6):1704—1711.

META S,CINSDIKICI M. Vehicle classification algorithm based on component analysis for single-loop inductive detector [J]. IEEE Transactions on Vehicular Technology,2010,59(6):2795—2805.

FAN J,SONG Y,FEI M R. ART2 neural network interacting with environment[J]. Neurocomputing,2008,72:170—176.

AlIA J B,SAIDIA L,HARRATHA S,et al. Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning[J]. Applied Acoustics,2018,132:167—181.


  • There are currently no refbacks.