Review of Bridge Apparent Defect Inspection Based on Machine Vision
Bridges are crucial infrastructure for traffic and transportation.The inspection of bridge apparent defects is important for ensuring public safety,extending the lifespan of bridges,and identifying risks in a timely manner.They also contribute to improving the reliability and durability of bridges during their operational phases.In recent years,with the rapid development of technologies such as computer vision and artificial intelligence,machine vision has gradually emerged as a new approach for bridge apparent defect inspection.This study conducted a detailed analysis of relevant studies in recent years to review the key techniques for bridge apparent defect inspection based on machine vision,including inspection platform development,data acquisition,image processing,3D reconstruction,defect localization,and defect parameter quantification techniques.By analyzing the inspection process of existing research,a technical framework for bridge apparent defect inspection based on machine vision was summarized,and the functions and connections between each process were analyzed.The above-mentioned review of key techniques and summary of technical frameworks provide a reference for researchers conducting inspection work on bridge structures.Finally,based on the different levels of automation in data acquisition and defect detection observed in existing studies,this study proposes a hierarchical classification for intelligent bridge apparent defect inspection based on machine vision.This classification includes six levels:manual inspection assistance,defect inspection and localization,partially automated inspection,globally automated inspection,high-degree automated inspection,and fully automated inspection.A comparison of existing literature reveals that although research has moved beyond the traditional stage of manual inspection,it still falls short of achieving fully automated inspection.Therefore,this field has strong research value and broad application pros-pects.
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