Road vehicle classification using machine learning techniques 机翻标题: 暂无翻译,请尝试点击翻译按钮。

会议集名/来源
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019: SPIE Smart Structures + Nondestructive Evaluation Conference, 4-7 March 2019, Denver, Colorado, United States
出版年
2019
页码
109700O.1-109700O.12
会议地点
Denver
作者单位
Department of Civil and Environmental EngineeringNorth Dakota State University, Fargo, ND, 58108-6050, U.S.ADepartment of Civil and Environmental EngineeringNorth Dakota State University, Fargo, ND, 58108-6050, U.S.A ying.huang@ndsu.edu
作者
Mu'ath Al-Tarawneh;Ying Huang
摘要
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.
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