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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. Our goal is to utilize small Unmanned Aircraft Systems (sUAS) and computer vision-based analysis to automate the inspection of the interior and exterior of NASA’s critical wind tunnel facilities. We detect missing fasteners as our defect class, and detect existing fasteners to provide potential future missing fastener sites for preventative maintenance. These detections are done on both 2D raw 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 (NFAC) at NASA Ames Research Center. Our method uses object detection to create image level bounding boxes of the fasteners and missing fasteners, then uses photogrammetry to create a mapping from 2D image locations to 3D real world locations. The image level bounding boxes and the 2D to 3D mapping are then combined to determine the 3D location of the defects. We describe the data collection, photogrammetry, and computer vision techniques used for object detection as well as a quantitative analysis of the method.