Search results for "low-cost technologies"

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Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

2020

Road pavement conditions have significant impacts on safety, travel times, costs, and environmental effects. It is the responsibility of road agencies to ensure these conditions are kept in an acceptable state. To this end, agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation. These systems, however, require accurate data. Currently, most agencies rely on manual distress surveys and as a result, there is significant research into quick and low-cost pavement distress identification methods. Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned a…

3D modelRoad pavement distreComputer science0211 other engineering and technologiesPoint cloud02 engineering and technologylcsh:TechnologyTransport engineeringData acquisition021105 building & construction0502 economics and businesslow-cost technologiesStructure from motionSettore ICAR/04 - Strade Ferrovie Ed AeroportiGeneral Materials ScienceSegmentationroad pavement distressstructure-from-motionCivil and Structural Engineering050210 logistics & transportationlcsh:T05 social sciencesLow-cost technologiePavement managementBuilding and ConstructionGeotechnical Engineering and Engineering Geology3d modelsComputer Science ApplicationsIdentification (information)Work (electrical)Mobile phoneInfrastructures
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Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

2020

Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to…

Damage detectionComputer science0211 other engineering and technologies02 engineering and technologylcsh:Technologylcsh:ChemistryTransport engineeringAutomated detectionSeverity assessmentRoad networks021105 building & constructionlow-cost technologies0202 electrical engineering electronic engineering information engineeringSettore ICAR/04 - Strade Ferrovie Ed AeroportiGeneral Materials ScienceRoad pavement distresseslcsh:QH301-705.5InstrumentationPavement management systemFluid Flow and Transfer Processeslcsh:Tbusiness.industryProcess Chemistry and TechnologyDeep learningLow-cost technologieGeneral EngineeringPavement managementDeep learningUrban roadIntegrated approachlcsh:QC1-999Computer Science ApplicationsWorkflowlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040020201 artificial intelligence & image processingArtificial intelligencelcsh:Engineering (General). Civil engineering (General)businesslcsh:PhysicsPavement condition monitoringApplied Sciences
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