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dc.contributor.advisorAzar, Ehsan Rezazadeh
dc.contributor.authorLopes Amaral Loures, Luana
dc.date.accessioned2021-10-07T17:30:10Z
dc.date.available2021-10-07T17:30:10Z
dc.date.created2021
dc.date.issued2021
dc.identifier.urihttps://knowledgecommons.lakeheadu.ca/handle/2453/4872
dc.description.abstractUnpaved roads have a significant role in Canada's transportation and service activities, accounting for close to 60% of Canada's total public road networks. Furthermore, they connect agricultural, mining, recreational areas, and small communities to the nearby towns and businesses. An effective maintenance program for a network of unpaved roads requires a detailed assessment of the road surface’s condition, and such assessment is usually made by visual inspections which can be time-consuming and error prone. The main part of these evaluations aims to identify distresses on the road surface, such as washboarding (corrugation), potholes, and rutting. Many research studies have developed methods to automate condition assessment of asphalt roads by combining machine learning algorithms and low-cost unmanned aerial vehicles (UAV), but the research on the automated assessment of unpaved roads is very limited. A system has been developed in this study to automate the assessment of unpaved roads by coupling computer vision methods, namely deep convolutional neural networks, and UAV-based imaging. This automated system could be used as an alternative method to reduce the need for human effort and possible manual errors, and therefore improve road maintenance programs in remote areas. The performance of the proposed method was evaluated using different test settings, and despite having some challenges, such as false positives, it showed promising outcomes that can contribute to the proposed purpose of this research. This proposed method has a potential for further improvement and the findings can be used as a basis for similar studies.en_US
dc.language.isoen_USen_US
dc.subjectAutomated assessment of unpaved roadsen_US
dc.subjectRoad surface evaluationen_US
dc.subjectRoad damage detectionen_US
dc.titleUnpaved roads’ surface evaluation using unmanned aerial vehicle and deep learning segmentationen_US
dc.typeThesisen_US
etd.degree.nameMaster of Scienceen_US
etd.degree.levelMasteren_US
etd.degree.disciplineEngineering : Civilen_US
etd.degree.grantorLakehead Universityen_US


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