| 基于深度卷积神经网络的裂纹检测方法综述 |
| |
| 引用本文： | 冉蓉,徐兴华,邱少华,崔小鹏,欧阳斌.基于深度卷积神经网络的裂纹检测方法综述[J].计算机工程与应用,2021,57(9):23-35. |
| |
| 作者姓名： | 冉蓉 徐兴华 邱少华 崔小鹏 欧阳斌 |
| |
| 作者单位： | 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 |
| |
| 基金项目： | 国家自然科学基金;国家部委科技重点实验室稳定支持科研项目 |
| |
| 摘 要： | 裂纹是威胁民用基础设施安全运行的重要因素之一，及时准确地检测出裂纹可以有效避免事故的发生。基于计算机视觉的自动裂纹检测方法具有操作简单、检测速度快、检测精度高的优点，被广泛应用于桥梁、道路监测、房屋建造、轨道交通等领域。总结了现有裂纹检测主要手段，详细介绍了三类基于深度卷积神经网络的裂纹检测方法，即基于分类的裂纹检测、基于目标检测的裂纹检测、基于像素级分割的裂纹检测，分析了基本原理、优缺点及其实际应用。汇总了裂纹检测领域常用数据集，并探讨了基于深度卷积神经网络的检测方法存在的问题，对其未来发展进行了展望。
|
| 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 |
Review of Crack Detection Methods Based on Deep Convolutional Neural Networks |
| |
| RAN Rong,XU Xinghua,QIU Shaohua,CUI Xiaopeng,OUYANG Bin.Review of Crack Detection Methods Based on Deep Convolutional Neural Networks[J].Computer Engineering and Applications,2021,57(9):23-35. |
| |
| Authors: | RAN Rong XU Xinghua QIU Shaohua CUI Xiaopeng OUYANG Bin |
| |
| Affiliation: | National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan 430033, China |
| |
| Abstract: | 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. |
| |
| Keywords: | surface crack detection computer vision deep learning image processing machine learning deep convolutional neural networks |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
