A Deep Neural Network Approach With Hyper-Parameter Optimization for Vehicle Type Classification Using 3D Magnetic Sensor

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.