Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours

TL;DR

This paper introduces FS-FSD, a Fourier-based method for representing bridge defect contours compactly, improving geometric accuracy and shareability over traditional bounding boxes and masks.

Contribution

The study proposes a novel Fourier contour descriptor regression method that enhances defect boundary representation in bridge inspection images.

Findings

FS-FSD outperforms baseline detection, segmentation, and contour methods in geometric accuracy.

Fourier contour records are more compact and recoverable than bounding boxes and raster masks.

The approach improves defect boundary preservation for engineering review and workflows.

Abstract

AI-assisted bridge defect inspection often produces bounding boxes with crude geometry or raster masks that are costly to store, 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. On 3,767 UAV-collected bridge images with 42,346 defect instances, FS-FSD achieves higher polygon-space accuracy and better matched-TP geometric quality than representative detection, segmentation, and contour baselines. These results show that, compared with bounding boxes and raster masks, Fourier contour records preserve defect-boundary geometry in a more compact, recoverable, and shareable form for engineering review and downstream information workflows. Future work will study the modeling of multi-region, fragmented, and adjacent bridge-defect boundaries and extend the framework toward long-term bridge-defect tracking and lifecycle-oriented management.