Road vehicle classification using machine learning techniques

Mu'ath Al-Tarawneh; Ying Huang

doi:10.1117/12.2514320

27 March 2019 Road vehicle classification using machine learning techniques

Mu'ath Al-Tarawneh, Ying Huang

Proceedings Volume 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019; 109700O (2019) https://doi.org/10.1117/12.2514320
Event: SPIE Smart Structures + Nondestructive Evaluation, 2019, Denver, Colorado, United States

Abstract

The vehicle classification system developed by Federal Highway Administration (FHWA) of United States divides vehicle type into 13 categories depending on the number of axles and the wheelbase. However, establishing a fixed threshold for classifying a vehicle is difficult. The overlapping between vehicles pattern in the system needs a pattern recognition technique to distinguish between different vehicle categories. In this study, machine learning algorithms were used to classify various vehicles based on the collected traffic data from the embedded three-dimension Glass Fiber-Reinforced Polymer packaged Fiber Bragg Grating sensors (3D GFRP-FBG). The investigated machine learning algorithms include the support vector machines (SVM), Neural Network, and k-nearest neighbors (KNN) algorithms.

Conference Presentation

Citation Download Citation

Mu'ath Al-Tarawneh and Ying Huang "Road vehicle classification using machine learning techniques ", Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 109700O (27 March 2019); https://doi.org/10.1117/12.2514320

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