Abstract

Wind tunnels are crucial facilities that support the aerospace industry. However, these facilities are large, complex, and pose unique maintenance and inspection requirements. Manual inspections to identify defects such as cracks, missing fasteners, leaks, and foreign objects are important but labor and schedule intensive. The goal of this thesis is to utilize small Unmanned Aircraft Systems with onboard cameras and computer vision-based analysis to automate the inspection of the interior and exterior of NASA's critical wind tunnel facilities. Missing fasteners are detected as the defect class, and existing fasteners are detected to provide potential future missing fastener sites for preventative maintenance. These detections are performed in both 2D on the images and in 3D space to provide a visual reference and real world location to facilitate repairs. A dataset was created consisting of images taken along a grid-like pattern of an interior tunnel section in the AEDC National Full-Scale Aerodynamics Complex at NASA Ames Research Center. To localize the defects, object detection was used to create image level bounding boxes of the fasteners and missing fasteners, and photogrammetry was used to create a correspondence of 3D real world locations and 2D image locations. The image level bounding boxes and the 2D to 3D correspondences are then combined to determine the 3D location of the defects. On the test data, the method was able to successfully localize all of the objects in 3D space with no false positives.

Notes

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Graduation Date

2020

Semester

Fall

Advisor

Sukthankar, Gita

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0008307; DP0023744

Language

English

Release Date

December 2020

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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