Construction Safety and Health Monitoring Using Unmanned Aerial Vehicles and Deep Learning

dc.contributor.advisorAwolusi, Ibukun G.
dc.contributor.authorAkinsemoyin, Aliu
dc.contributor.committeeMemberChakraborty, Debaditya
dc.contributor.committeeMemberCai, Jiannan
dc.contributor.committeeMemberRichardson, Walter B.
dc.date.accessioned2024-01-26T20:46:05Z
dc.date.available2023-08-15
dc.date.available2024-01-26T20:46:05Z
dc.date.issued2022
dc.description.abstractConstruction is a high-risk industry characterized by many factors that are potentially hazardous to workers. The continuous monitoring of unsafe behaviors and conditions has been identified as a proactive and active means of eliminating potential safety and health hazards on construction sites. Digital technologies combined with deep learning and computer vision can be applied to create a robust learning environment and enhance the analysis of safety data for generating insights needed to improve safety performance. This study presents the development and validation of a framework that implements the use of Unmanned Aerial Vehicles (UAVs) and deep learning for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework is validated using a pilot case study. Digital images of construction safety activities are collected on active construction sites and an integrated computer vision and deep learning model is used to extract relevant features from the collected data. This study provides valuable findings that can be utilized to improve decision-making in safety management because rapid collection and analysis of safety and health data would enable safety personnel to take faster preventive actions to avoid future accidents.
dc.description.departmentCivil and Environmental Engineering
dc.format.extent52 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2568
dc.languageen
dc.subjectConstruction
dc.subjectDeep learning
dc.subjectSafety
dc.subjectUAV
dc.subject.classificationCivil engineering
dc.titleConstruction Safety and Health Monitoring Using Unmanned Aerial Vehicles and Deep Learning
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentCivil and Environmental Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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