Vision-Based Autonomous Quadrupedal Robot for Rapid Post-Earthquake Crack-Based Building Damage Detection

Abstract

Rapid and reliable crack-based visual damage detection after earthquakes is crucial for safe and effective disaster response. Manual inspections are often slow and hazardous for engineers in unstable structures. This study proposes a quadrupedal robotic inspection system for rapid post-earthquake crack-based visual damage detection in reinforced concrete structures. A Unitree Go2 robot equipped with an Intel RealSense D435i RGB-D camera collected a dataset of 3255 annotated crack images from both field and public sources. The YOLOv8n model, trained and deployed on an NVIDIA Jetson AGX Xavier, demonstrated high detection performance in laboratory tests on reinforced concrete specimens, with precision, recall, and mAP@50 values all exceeding 85%. The system provides fast, accurate, and automated structural health assessments, reducing human risk and improving inspection efficiency in hazardous post-disaster environments. Future work will focus on expanding damage detection capabilities and real-world deployment.