Deep learning approaches for vehicle type classification with 3-D magnetic sensor

KOLUKISA B., Yildirim V. C., Elmas B., Ayyildiz C., GÜNGÖR V. Ç.

Computer Networks, vol.217, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 217
  • Publication Date: 2022
  • Doi Number: 10.1016/j.comnet.2022.109326
  • Journal Name: Computer Networks
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: Intelligent Transportation Systems, Magnetic sensor, Vehicle classification, Deep learning, ROBUST
  • Kayseri University Affiliated: No


© 2022 Elsevier B.V.In the Intelligent Transportation Systems, it is crucial to determine the type of vehicles to improve traffic management, increase human comfort, and enable future development of transport infrastructures. This paper presents a deep learning-based vehicle type classification approach for intermediate road traffic. Specifically, a low-cost, easy-to-install, battery-operated 3-D traffic sensor is designed and developed. In addition, a total of 376 vehicle samples are collected, and the vehicles are identified into three different classes according to their structures: light, medium, and heavy. Firstly, an oversampling method is applied to increase the number of samples in the training set. Then, the signals are converted into time series for LSTM and GRU and 2-D images for transfer learning models. Finally, soft voting is proposed using the LSTM, GRU, and VGG16, which is the best transfer learning method for vehicle type classification. The developed system is portable, power-limited, battery-operated, and reliable. Comparative performance results show that the soft voting ensemble method using a deep learning classifier improves the accuracy and f-measure performances by 92.92% and 93.42%, respectively. Additionally, the battery lifetime of the developed magnetic sensor node can work for up to 2 years.