基于深度学习的结构裂缝检测综述

A review of structural crack detection based on deep learning

将无人机影像数据与深度学习技术相结合,可实现水工建筑物表面裂缝的远程非接触式检测,为维护结构正常运行提供保障。本文介绍了近年来流行的深度学习方法,梳理了基于卷积神经网络的分类、检测和分割三类算法在裂缝检测中的应用及特点。结合堤防裂缝检测,阐述了无人机影像用于裂缝检测的流程和挑战。最后给出水工建筑物裂缝检测的研究难点与展望。

Combining UAV image data with deep learning technology can realize remote non-contact detection of cracks on the surface of hydraulic buildings,providing a guarantee for maintaining the normal operation of the structure.This paper introduces the popular deep learning methods in recent years,and compares the applications and characteristics of three types of algorithms based on convolutional neural networks for classification,detection and segmentation in crack detection.Combined with an embankment crack detection example,the process and challenges of using UAV images for crack detection are explained.Finally,the difficulties and outlook for crack detection research in hydraulic structures are given in the paper.