Author ORCID Identifier
Date Available
4-26-2019
Year of Publication
2019
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College
Engineering
Department/School/Program
Computer Science
Advisor
Dr. Nathan Jacobs
Abstract
Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-flow speed. Inference speeds for our methods are mere milliseconds, enabling large-scale roadway study at a fraction of the cost of manual approaches.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2019.136
Funding Information
This research was supported by NSF CAREER grant IIS-1553116 and computing resources provided by the University of Kentucky Center for Computational Sciences, including a hardware donation from IBM.
Recommended Citation
Song, Weilian, "Image-Based Roadway Assessment Using Convolutional Neural Networks" (2019). Theses and Dissertations--Computer Science. 78.
https://uknowledge.uky.edu/cs_etds/78
