Rapid Identification and Visual Localization Method for Few-Shot Inspection of Bolt Defects in Bridge Tall Components

To enhance the efficiency of bridge tall component inspections and achieve rapid detection and accurate localization of defects,this study proposed a few-shot inspection algorithm for bridge bolt corrosion using Unmanned Aerial Vehicle (UAV) imagery and deep learning techniques.UAVs were employed to capture images of hard-to-reach areas by flying along a customized low-altitude path.This path was specifically designed to collect visual data at fixed angles,distances,and postures,ensuring the temporal consistency necessary for analysis.The study developed a corrosion detection method for bridge bolts based on the You Only Look Once version 8 (YOLOv8),tailored for few-shot datasets.Comparative experiments with multiple baseline models demonstrate that the proposed approach achieves superior performance,with an accuracy exceeding 90%,even with limited annotated sample numbers.Combining the collected image data with a digital model of the bridge enabled the precise visualization and localization of bolt defects in three-dimensional space.This method offers an efficient solution for the maintenance and inspection of difficult-to-reach bridge tall components,contributing to the long-term durability and safety of bridge structures.