A deep neural network approach with hyper-parameter optimization for vehicle type classification using 3-D magnetic sensor

Kolukisa B., Yildirim V. C., Ayyildiz C., Gungor V. C.

COMPUTER STANDARDS & INTERFACES, vol.84, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 84
  • Publication Date: 2023
  • Doi Number: 10.1016/j.csi.2022.103703
  • Journal Name: COMPUTER STANDARDS & INTERFACES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Linguistic Bibliography, Metadex, Civil Engineering Abstracts
  • Kayseri University Affiliated: No

Abstract

The identification of vehicle types plays a critical role in Intelligent Transportation Systems. In this study, battery-operated, easy-to-install, low-cost 3-D magnetic traffic sensors have been developed for vehicle type classification problems. In addition, a new machine learning approach based on deep neural networks (DNN) with hyper-parameter optimization using feature selection and extraction methods has been proposed for vehicle type classification. A dataset is collected from the field, and vehicles are classified into three different classes, i.e., light: motorcycles, medium: passenger cars, and heavy: buses, based on vehicle structures and sizes. The proposed system is portable, energy-efficient, and reliable. The performance results show that the proposed method, which is based on a DNN classifier, has an accuracy of 91.15%, an f-measure of 91.50%, and a battery life of up to 2 years.