Target recognition using neural networks for wind tunnel model deformation measurements

Optical measurement techniques in use at NASA wind tunnels provide a non-invasive method for measuring model deformation, but analyzing the data can be quite challenging. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. A technique using neural network and image processing technologies increases the reliability of target recognition, reduces model preparation time, and increases productivity at NASA wind tunnels.