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035 a| (Sirsi) o1268150286;

035 a| (OCoLC)1268150286;

049 a| EREE;

090 a| TE250;

100 1 a| Olufowobi, Kehinde,e| author.?| UNAUTHORIZED;

245 10 a| Towards a low-cost vision system for real-time pavement condition assessment /c| by Kehinde Olufowobi.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2021.;

300 a| 1 online resource (79 pages) :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text file;

347 b| PDF;

347 c| 14.32 MB;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2021;

500 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Nic Herndon;

500 a| Title from PDF t.p. (viewed August 24, 2022).;

520 3 a| Pavement condition assessment is typically performed through manual inspections and specialized hardware and software. Although advances in camera and sensing technology in the last decade helped propel the automation of pavement distress detection and characterization, increased equipment acquisition and running costs limit access to the most effective solutions. Furthermore, some of these advanced techniques require substantial human involvement to process and analyze data correctly. This thesis proposes a cost-effective, end-to-end automated approach to pavement condition assessment that employs a neural object detector to identify and measure instances of pavement distress in real time from oblique 2D imagery acquired using a UAV. To promote ease of implementation and scale, the associated modeling process is simplified by using Google Street View data as a proxy for data collected via a UAV-mounted camera. A state-of-the-art object detector architecture is applied to identify and localize pavement distress instances in monocular images. Camera data, information about Street View image acquisition conditions, and the principles of photogrammetry and planar homography are combined to construct a mapping for translating pixel distances to real-world distances. This capability is then integrated into the neural network inference process to derive an end-to-end system for real-time distress identification and measurement.;

504 a| Includes bibliographical references.;

650 0 a| Pavementsx| Quality control.=| ^A102073;

650 0 a| Risk assessment.=| ^A221641;

650 0 a| Computer vision.=| ^A162076;

653 a| Object detection;

653 a| planar homography;

653 a| pavement condition assessment;

655 7 a| Academic theses.2| fast0| (OCoLC)fst01726453;

655 7 a| Academic theses.2| lcgft;

655 7 a| Thèses et écrits académiques.2| rvmgf0| (CaQQLa)RVMGF-000001173;

655 2 a| Academic Dissertation.0| (DNLM)D019478?| UNAUTHORIZED;

700 1 a| Herndon, Nic,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 u| http://hdl.handle.net/10342/9421z| Access via ScholarShip;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5668835;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5668835-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 9/14/2021x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5668835-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 9/14/2021x| ETDz| HERESOURCE;

Towards a low-cost vision system for real-time pavement condition assessment / by Kehinde Olufowobi.

Author/creator	Olufowobi, Kehinde author.
Other author	Herndon, Nic, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2021.
Description	1 online resource (79 pages) : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Pavements--Maintenance and repair. --Management. --Data processing. Pavements--Quality control. Risk assessment. Computer vision.

Summary	Pavement condition assessment is typically performed through manual inspections and specialized hardware and software. Although advances in camera and sensing technology in the last decade helped propel the automation of pavement distress detection and characterization, increased equipment acquisition and running costs limit access to the most effective solutions. Furthermore, some of these advanced techniques require substantial human involvement to process and analyze data correctly. This thesis proposes a cost-effective, end-to-end automated approach to pavement condition assessment that employs a neural object detector to identify and measure instances of pavement distress in real time from oblique 2D imagery acquired using a UAV. To promote ease of implementation and scale, the associated modeling process is simplified by using Google Street View data as a proxy for data collected via a UAV-mounted camera. A state-of-the-art object detector architecture is applied to identify and localize pavement distress instances in monocular images. Camera data, information about Street View image acquisition conditions, and the principles of photogrammetry and planar homography are combined to construct a mapping for translating pixel distances to real-world distances. This capability is then integrated into the neural network inference process to derive an end-to-end system for real-time distress identification and measurement.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Nic Herndon
General note	Title from PDF t.p. (viewed August 24, 2022).
Dissertation note	M.S. East Carolina University 2021
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.
Genre/form	Academic theses.
Genre/form	Academic theses.
Genre/form	Thèses et écrits académiques.

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Towards a low-cost vision system for real-time pavement condition assessment / by Kehinde Olufowobi.

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