FS-FSD regresses frequency-supervised Fourier contours for bridge defects, yielding higher polygon accuracy and better geometric quality than box, mask, or contour baselines on 3,767 UAV images with 42,346 instances.
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Deep neural networks are trained to recover low-order Fourier elliptical components describing overall shape and orientation from simulated transit light curves of arbitrary 2D objects.
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Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours
FS-FSD regresses frequency-supervised Fourier contours for bridge defects, yielding higher polygon accuracy and better geometric quality than box, mask, or contour baselines on 3,767 UAV images with 42,346 instances.
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Beyond Spherical geometry: Unraveling complex features of objects orbiting around stars from its transit light curve using deep learning
Deep neural networks are trained to recover low-order Fourier elliptical components describing overall shape and orientation from simulated transit light curves of arbitrary 2D objects.