Building a pothole detection and tracking system
Date
2019
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Abstract
Building and maintaining infrastructure is often a key challenge in developing countries,
and Ghana is no exception. Increasing population and car ownership rates coupled with
poor maintenance cultures result in a corresponding increase in the rate of damage of
roads, causing deformities such as cracks and potholes. These road deformities not only
negatively impact a country’s road infrastructure and the cars which ply said roads, but
also pose a threat to road users. In Ghana, only two mobile maintenance units are charged
with monitoring the roads in all ten regions of the country. Thus, this project presents
Pothole Tracker Ghana, a two-tiered application inspired by the idea of crowdsourcing.
Consisting of a vision-based pothole classification system implemented on a Raspberry Pi
and a map-based web application, this project aims to reduce the barriers to data collection
on poor road infrastructure on the part of governments whilst allowing everyday road
users to make informed decisions concerning their journeys. Three different algorithms are
considered and compared for the classification task; logistic regression, support vector
machines (SVM) and a hybrid algorithm incorporating a convolutional neural network (CNN) and SVM. The tuned SVM is chosen for the final system implementation.
Description
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, April 2019
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Capstone Project
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Keywords
Ghana, pothole detection, road classification, vision-based machine learning approach, Support Vector Machine (SVM)