用傅里叶轮廓精准表示桥梁缺陷，更省空间且可复原。
Contour-Native Bridge Defect Detection and Compact Digital Archiving with Frequency-Supervised Fourier Contours
- 直接回归傅里叶轮廓描述符，实现向量级缺陷表征。
- 在3767张无人机图像上，几何精度优于基线方法。
- 适合工程审图与长期桥梁缺陷追踪场景。
AI辅助的桥梁缺陷检测常生成几何粗糙的边界框或存储成本高的栅格掩码。本研究探索如何将缺陷以紧凑、可恢复的图像空间轮廓向量形式记录。提出频率监督傅里叶轮廓检测（FS-FSD），直接回归傅里叶轮廓描述符，并在统一多边形空间协议下评估边界框、掩码与轮廓。在3,767张无人机采集的桥梁图像、共42,346个缺陷实例上，FS-FSD在多边形空间精度和匹配真阳性几何质量方面均优于代表性检测、分割及轮廓基线。结果表明，相比边界框与栅格掩码，傅里叶轮廓记录能以更紧凑、可恢复、易共享的形式保留缺陷边界几何信息，适用于工程审查与下游信息流程。未来工作将研究多区域、断裂及邻近缺陷边界的建模，并拓展框架至长期桥梁缺陷追踪与全生命周期管理。
原文摘要 · Abstract (English)
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.
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