基于机器视觉法的桥梁表观病害检测研究综述

Review of Bridge Apparent Defect Inspection Based on Machine Vision

桥梁作为重要的基础设施,承担着道路交通和人员、货物运输等重要任务。桥梁表观病害的及时有效检测具有确保公共安全、延长桥梁使用寿命、及时排查风险等重大意义,有助于提高桥梁服役阶段的可靠性和耐久性。近年来,随着计算机视觉、人工智能等技术的快速发展,机器视觉法逐渐成为了桥梁表观病害检测的新兴手段之一。首先,通过详细分析近年来该领域的多篇相关文献,综述了基于机器视觉法进行桥梁表观病害检测的关键技术,包括检测平台研发技术、数据采集技术、图像处理技术、三维建图技术、病害定位技术和病害参数量化技术等。其次,通过分析现有研究开展检测工作的流程,总结了基于机器视觉法进行桥梁表观病害的技术框架,并分析了其中各个流程之间的功能与联系。上述关键技术的综述与技术框架的总结可为研究者开展检测工作提供一定的参考。最后,根据现有研究在实施检测任务时自动化程度的不同,提出了基于机器视觉法进行桥梁表观病害检测的智能化分级,包括人工检测辅助、病害定位检测、局部自动检测、整体自动检测、高度自动检测和完全自动检测6个等级。对比文献研究可知,现有研究虽然已经脱离了传统的人工检测的阶段,但仍与完全自动检测具有一定的差距。该领域仍具有较强的研究价值与广阔的应用前景。

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 prospects.