AI RESEARCH
Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours
arXiv CS.CV
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ArXi:2605.08781v1 Announce Type: new AI-assisted bridge defect inspection often produces bounding boxes with crude geometry or raster masks that are costly to, transmit, and reuse. This study investigates how detected defects can be represented as compact, recoverable contour-level vector records in image space. We propose Frequency-Supervised Fourier Series Detection (FS-FSD), which directly regresses Fourier contour descriptors and evaluates boxes, masks, and contours under a unified polygon-space protocol.