用AI检测桥梁损伤并模糊施工标志，保护地区隐私
Multi-stage Bridge Inspection System: Integrating Foundation Models with Location Anonymization
- 用SAM3模型检测钢筋锈蚀，DBSCAN补全遗漏区域
- 4种预处理提升文字识别准确率，单图处理仅需1.7秒
- 开源系统兼顾检测精度与隐私保护，适合基建巡检
日本规定每五年对土木基础设施进行一次视觉检查。现场拍摄的损伤图像常包含混凝土裂缝和钢筋裸露，且伴有揭示地域信息的施工标识。为确保基础设施安全使用的同时避免公众焦虑，必须在准确提取损伤特征并可视化修复决策指标的前提下，保护地域信息。本文提出一个开源桥梁损伤检测系统，具备区域隐私保护能力。采用分割任意模型（SAM3）进行钢筋腐蚀检测，并利用DBSCAN自动补全缺失区域。通过高斯模糊处理施工标识区域以实现隐私保护。四种预处理方法提升了光学字符识别（OCR）的准确性，结合GPU优化，单张图像处理时间仅为1.7秒。技术栈包括SAM3、PyTorch、OpenCV、pytesseract和scikit-learn，实现了高效且隐私安全的桥梁巡检。
原文摘要 · Abstract (English)
In Japan, civil infrastructure condition monitoring is mandated through visual inspection every five years. Field-captured damage images frequently contain concrete cracks and rebar exposure, often accompanied by construction signs revealing regional information. To enable safe infrastructure use without causing public anxiety, it is essential to protect regional information while accurately extracting damage features and visualizing key indicators for repair decision-making. This paper presents an open-source bridge damage detection system with regional privacy protection capabilities. We employ Segment Anything Model (SAM) 3 for rebar corrosion detection and utilize DBSCAN for automatic completion of missed regions. Construction sign regions are detected and protected through Gaussian blur. Four preprocessing methods improve OCR accuracy, and GPU optimization enables 1.7-second processing per image. The technology stack includes SAM3, PyTorch, OpenCV, pytesseract, and scikit-learn, achieving efficient bridge inspection with regional information protection.
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