[1]郎洪,温添,陆键,等.基于深度学习的三维路面裂缝类病害检测方法[J].东南大学学报(自然科学版),2021,51(1):53-60.[doi:10.3969/j.issn.1001-0505.2021.01.008]
 Lang Hong,Wen Tian,Lu Jian,et al.3D pavement crack detection method based on deep learning[J].Journal of Southeast University (Natural Science Edition),2021,51(1):53-60.[doi:10.3969/j.issn.1001-0505.2021.01.008]
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基于深度学习的三维路面裂缝类病害检测方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
51
期数:
2021年第1期
页码:
53-60
栏目:
交通运输工程
出版日期:
2021-01-20

文章信息/Info

Title:
3D pavement crack detection method based on deep learning
作者:
郎洪1温添1陆键 1丁朔1陈圣迪2
1 同济大学道路与交通工程教育部重点实验室, 上海 201804; 2 上海海事大学交通运输学院, 上海 201306
Author(s):
Lang Hong1 Wen Tian1 Lu Jian1 Ding Shuo1 Chen Shengdi2
1 Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2 College of Transportation Engineering, Shanghai Maritime University, Shanghai 201306, China
关键词:
道路工程 裂缝检测 三维图像 卷积神经网络 高程检查
Keywords:
road engineering crack detection 3D image convolutional neural networks depth-checking
分类号:
U416.2
DOI:
10.3969/j.issn.1001-0505.2021.01.008
摘要:
为了在路面三维图像的基础上快速、准确、完整地识别裂缝,提出一种基于深度学习的路面裂缝类病害自动检测方法.首先,以子块图像为处理单元,将三维图像划分为裂缝面元和背景面元,其中背景面元包含了路面标线、不同纹理和桥接缝等复杂场景.根据对面元图像的分析,提出一种基于卷积神经网络的PCCNet分类模型,用于路面背景面元和路面裂缝面元的自动识别.然后,为了进一步提取裂缝面元内裂缝的完整轮廓,考虑路面三维图像中裂缝像素级邻域特征,利用PCCNet模型结合裂缝高程检查方法对路面裂缝进行检测.研究结果表明:通过训练集4 300张高精度三维图像的训练,模型在3 850次迭代之后出现过拟合,且此时PCCNet模型在验证集上的总体F值达到最大,为92.9%;将PCCNet模型结合裂缝高程检查方法应用在测试集的200张三维图像上,方法准确率、召回率和F值分别为87.8%、90.1%和88.9%.与改进Canny方法和种子识别方法对比,所提出的方法在抑制噪声和检测细小裂纹方面具有更强的鲁棒性.
Abstract:
In order to detect cracks quickly, accurately and completely on the basis of three-dimensional(3D)pavement images, an automatic detection method of pavement cracks based on the deep learning model is proposed. Firstly, taking the sub-block image as the processing unit, the 3D image is divided into crack patches and background patches, in which the background patch contains complex scenes such as pavement marking, different textures and bridge joints. A pavement crack classification network(PCCNet)based on the convolutional neural network(CNN)is proposed for the automatic recognition of pavement background patch and pavement crack patch. Then, in order to further extract the complete contour of cracks, considering the pixel-level neighborhood features of cracks in 3D pavement images, the PCCNet combined with the crack depth-checking method is used to detect pavement cracks. The results show that through the training of 4 300 high-precision 3D images in the training set, the model is over fitted after 3 850 iterations, and the overall F-value of the PCCNet on the verification set reaches the maximum, which is 92.9%; the PCCNet combined with the depth-checking method is applied to 200 3D images in the testing set, and the accuracy rate, recall rate and F-value are 87.8%, 90.1% and 88.9%, respectively. Compared with the improved Canny algorithm and the seed recognition method, the proposed method has stronger robustness in suppressing noise and detecting small cracks.

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备注/Memo

备注/Memo:
收稿日期: 2020-08-04.
作者简介: 郎洪(1994—),男,博士生;陆键(联系人),男,教授,博士生导师,jianjohnlu@sina.com.
基金项目: 国家自然科学基金资助项目(71871165).
引用本文: 郎洪,温添,陆键,等.基于深度学习的三维路面裂缝类病害检测方法[J].东南大学学报(自然科学版),2021,51(1):53-60. DOI:10.3969/j.issn.1001-0505.2021.01.008.
更新日期/Last Update: 2021-01-20