"Sensor-Based Vehicle Classification Using Machine Learning" by Luke McFadden

Luke McFadden

Date of Award

3-2024

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Department of Electrical and Computer Engineering

First Advisor

Brett J. Borghetti, PhD

Abstract

This research investigates the classification of vehicles into heavy and light categories using acoustic, seismic, and magnetic sensor data. The effectiveness of using frequency domain data and classical machine learning techniques, is compared with the effectiveness of using time-series data and neural networks. The primary aim in doing so was to understand if modern neural network architectures could effectively remove the need for more traditional frequency based signals processing. A significant deliverable of this thesis was the feature importance determined for each of the three phenomenological types found within the data (acoustic, seismic, and magnetic). By analyzing the importance of features derived from acoustic, seismic, and magnetic sensors, the research provided insights into which sensor types and their specific characteristics were most critical for distinguishing between heavy and light vehicles.

AFIT Designator

AFIT-ENG-MS-24-M-184

Comments

A 12-month embargo was observed for posting this work on AFIT Scholar.

Distribution Statement A, Approved for Public Release. PA case number on file.

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