基于深度学习的基础设施表面裂纹检测方法研究进展  

Research Advance of Crack Detection for Infrastructure Surfaces Based on Deep Learning

作　　者：胡翔坤 李华[1,2] 冯毅雄 钱松荣 李键[1] 李少波[1] HU Xiangkun;LI Hua;FENG Yixiong;QIAN Songrong;LI Jian;LI Shaobo(State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China;Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China;State Key Laboratory of Fluid Power&Mechatronic Systems,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]贵州大学公共大数据国家重点实验室,贵阳550025 [2]清华大学机械工程系,北京100084 [3]浙江大学流体动力基础件与机电系统全国重点实验室,杭州310058

出　　处：《计算机工程与应用》2025年第1期1-23,共23页Computer Engineering and Applications

基　　金：国家自然科学基金(52205092);国家重点研发计划(2020YFB1713300);中国博士后科学基金面上项目(2023M731939);贵州大学自然科学专项(特岗)科研基金((2021)27);贵州大学人才项目([2020]25)。

摘　　要：民用基础设施在长期使用后容易发生物理结构或性能状态的改变,对其功能和使用安全造成一定的损害,因此,对这类设施的结构健康监测是十分必要的。裂纹检测是结构健康监测中极其重要的一部分,及时检测并识别这类损伤,能有效避免事故的发生。基于计算机视觉的表面裂纹检测方法操作简单、检测速度快、准确率高,被广泛应用于民用基础设施的表面裂纹检测。从图像分类、目标检测、语义分割三个不同的检测方向综述了基于深度学习的基础设施表面裂纹检测方法,总结了常见的数据采集方法和常用的公共裂纹数据集。最后讨论了基于深度学习的基础设施表面裂纹检测方法存在的困难与挑战,并展望了未来可能的发展方向。Civil infrastructure is prone to changes in physical or performance after long-term use,and causing certain damage to the function and service safety.So it is essential to monitor structure healthy of such facilities.Crack detection is an extremely important part of structure healthy monitoring.Timely detection and identification of such damage can effectively avoid severe accidents.Crack detection methods based on computer vision are simple,fast and accurate,and are widely used for surface crack detection in civil infrastructures.This paper reviews crack detection methods for infrastructure surfaces based on deep learning from three different detection directions:image classification,object detection,and semantic segmentation.And common data collection methods and commonly used public crack datasets are summarized.Finally,the difficulties and challenges of deep learning-based surface crack detection methods for infrastructures are discussed,and possible future development directions are envisioned.

关 键 词：结构健康监测 裂纹检测 计算机视觉 深度学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]