Multi-stage Bridge Inspection System: Integrating Foundation Models with Location Anonymization

TL;DR

This paper introduces an open-source bridge inspection system that combines advanced damage detection with regional privacy protection, enabling accurate assessment while safeguarding sensitive location information.

Contribution

It integrates foundation models with privacy-preserving techniques, providing an efficient and open-source solution for infrastructure monitoring with regional data protection.

Findings

SAM3 achieves accurate rebar corrosion detection

Gaussian blur effectively protects regional information

GPU optimization enables real-time processing

Abstract

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.