Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (9): 23-35.DOI: 10.3778/j.issn.1002-8331.2012-0500

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Review of Crack Detection Methods Based on Deep Convolutional Neural Networks

RAN Rong, XU Xinghua, QIU Shaohua, CUI Xiaopeng, OUYANG Bin   

  1. National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan 430033, China
  • Online:2021-05-01 Published:2021-04-29



  1. 海军工程大学 舰船综合电力技术国防科技重点实验室,武汉 430033


Crack is one of the most important factors threatening the safety of civil infrastructure, timely and accurate surface crack detection can effectively avoid possible accidents. Due to the advantages of simple operation, fast detection speed and high accuracy, Deep Convolutional Neural Networks(DCNN) based crack detection methods are now widely used in the structural monitoring fields of bridges, roads monitoring, building constructions and railway transportation etc. This paper summarizes the existing major crack detection methods and reviews DCNN-based crack detection methods in three ways:classification based, object detection based and segmentation based methods. Their principles, advantages and disadvantages, practical application are also analyzed. This paper introduces the commonly-used datasets in crack detection, and discusses the potential problems and future development of DCNN-based crack detection methods.

Key words: surface crack detection, computer vision, deep learning, image processing, machine learning, deep convolutional neural networks



关键词: 裂纹检测, 计算机视觉, 深度学习, 图像处理, 机器学习, 深度卷积神经网络