基于卷积神经网络的混凝土路面裂缝检测

摘要/Abstract

摘要： 混凝土道路路面中,裂缝的出现常常会导致重大的工程和经济问题。目前,利用计算机视觉技术进行裂缝检测时,需人工预先设计特征提取器对提取的图像特征进行分类,导致泛化能力较差和分类性能较弱。文中提出了一种基于卷积神经网络的裂缝检测方法,实现路面缺陷的自动化检测分类并提高路面裂缝检测效率与精度。首先,设计混凝土路面裂缝卷积神经网络,该模型基于AlexNet网络架构,从网络结构层次和超参数两个方面进行优化设计;其次,采用相机收集混凝土路面图像以获得学习数据,根据数据集大小、图像颜色因子的不同,分别创建了10000和20000张的灰色图与彩色RGB图4个数据集;然后,使用创建的4个数据集对设计的混凝土裂缝卷积神经网络进行训练,创建裂缝检测模型并与原始AlexNet模型相比较;最后,通过数据集大小、图像颜色因子与网络结构和超参数等影响因素对比两个模型。实验结果表明,通过增大数据集、使用彩色RGB图、调整网络结构和超参数,所提模型有助于提高分类检测精度。与原始AlexNet网络模型相比,所提网络模型的识别准确率更高,对彩色图像样本的识别准确率最高可达98.5%,同时避免了图像灰度的预处理,提高了裂缝检测的工作效率。

关键词: 道路路面, 卷积神经网络, 裂缝检测, 深度学习, 图像分类

Abstract: In concrete road pavements,the presence of cracks often leads to major engineering and economic problems.At present,when computer vision technology is used to conduct crack detection,artificial predesigned feature extractor is needed to extract image features for classification,resulting in poor generalization ability and classification perfor-mance.In this paper,a crack detection method based on convolutional neural network was proposed to realize the automatic detection and classification of pavement defects and improve the efficiency and accuracy of pavement crack detection.Firstly,the crack convolutional neural network of concrete pavement is designed.The model is based on AlexNet network architecture,and the model is optimized from two aspects:network structure level and hyperparameter.Secondly,the camera collects the concrete pavement image to obtain the learning data.According to the data set size and the image color factor,10000 and 20000 gray maps and four data sets of the color RGB map are respectively created.Then,the created four datasets are used.The data set trains the designed concrete crack convolutional neural network to create a crack detection model and compare it to the original AlexNet model.Finally,the two models are compared by factors such as dataset size,image color factor,network structure and hyperparameters.The experimental results show that by increasing the data set,using the color RGB map,adjusting the network structure and hyperparameters,the proposed model is helpful to improve the classification detection accuracy.Compared with the original AlexNet network model,the network model identification accuracy is high,and the recognition accuracy of color image samples is up to 98.5%.At the same time,the image gray level preprocessing is avoided and the efficiency of crack detection is improved.

Key words: Convolutional neural network, Crack detection, Deep learning, Image classification, Rroad pavement

中图分类号:

TP391

王丽苹, 高瑞贞, 张京军, 王二成. 基于卷积神经网络的混凝土路面裂缝检测[J]. 计算机科学, 2019, 46(11A): 584-589. https://doi.org/

WANG Li-ping, GAO Rui-zhen, ZHANG Jing-jun, WANG Er-cheng. Crack Detection of Concrete Pavement Based on Convolutional Neural Network[J]. Computer Science, 2019, 46(11A): 584-589. https://doi.org/

参考文献

[1]OLIVERA H,CORREIA P L.Supervised crack detection andclassification in images of road pavement flexible surfaces[M].Recent Advances in Signal Processing.InTech,2009. [2]JAHANSHAHI M R,JAZIZADEH F,MASRI S F,et al.Anunsupervised approach for autonomous pavement defect detection and quantification using an inexpensive depth sensor[J].Journal of Computing in Civil Engineering,2013,27(6):743-754. [3]ZHANG D,QU S,HE H,et al.Automatic ridgelet image enhancement algorithm for road crack image based on fuzzy entropy and fuzzy divergence[J].Opt.Lasers Eng.,2009,47 (11):1216-1225. [4]ZHANG Y.The design of glass crack detection system based on image pre-processing technology[C]∥Proceedings of IEEE 7th Joint International Information Technology and Artificial Intelligence Conference.Chongqing,China,2014:39-42. [5]彭博,蒋阳升,蒲云.基于数字图像处理的路面裂缝自动分类算法[J].中国公路学报,2014,27(9):10-18,24. [6]宋宏勋,马建,王建锋,等.基于双相机立体摄影测量的路面裂缝识别方法[J].中国公路学报,2015,28(10):18-25,40. [7]ZOU Q,CAO Y,LI Q,et al.CrackTree:automatic crack detection from pavement images[J].Pattern Recogn.Lett.,2012,33(3):227-238. [8]TALAB A M A,HUANG Z,XI F,et al.Detection crack in image using Otsu method and multiple filtering in image processing techniques [J].Optik,2016,127(3):1030-1033. [9]CHEN J H,SU M C,CAO R,et al.A self organizing map optimization based image recognition and processing model for bridge crack inspection[J].Automation in Construction,2017,73:58-66. [10]LIU Z,SHAHREL A,OHASHI T,et al.Tunnel crack detection and classification system based on image processing[C]∥Proc.SPIE.San Jose,California,USA,2002:145-152. [11]SAAR T,TALVIK O.Automatic asphalt pavement crack detection and classification using neural networks[C]∥Proceedings of the 12th Biennial Baltic Electronics Conference.Tallinn,Estonia,2010:345-348. [12]MOKHTATI S,WU L,YUN H B.Comparison of supervisedclassification techniques for vision based pavement crack detection[J].Transportation Research Record:Journal of the Transportation Research Board,2016,2595:119-127. [13]王睿,漆泰岳.基于机器视觉检测的裂缝特征研究[J].土木工程学报,2016,49(7):123-128. [14]LINS R G,GIVIGI S N.Automatic crack detection and measurement based on image analysis[J].IEEE Trans.Instrum.Meas.,2016,65 (3):583-590. [15]BRAY J,VERMA B,LI X,et al.A neural network based technique for automatic classification of road cracks[C]∥Proceedings of IEEE International Joint Conference on Neural Network.Vancouver,BC,Canada,2006:907-912. [16]ZHANG L,YANG F,ZHANG Y,et al.Road crack detection using deep convolutional neural network[C]∥Proceedings of IEEE International Conference on Image Processing.Phoenix,AZ,USA,2016:3708-3712. [17]CHA Y J,CHOI W,BUYUKOZTURK O.Deep learning-based crack damage detection using convolutional neural networks[J].Comput.Aided Civ.Inf.Eng.,2017,32 (5):361-378. [18]赵雪峰,李生元,欧进萍.基于人工智能与智能手机的混凝土裂纹检测[J].物联网技术,2017,7(8):15-18. [19]XIE D,ZHANG L,BAI L.Deep learning in visual computing and signal processing[J].Applied Computational Intelligence and Soft Computing,2017(10):1-13. [20]FENG C,LIU M Y,KAO C C,et al.Deep active learning forcivil infrastructure defect detection and classification[C]∥International Workshop on Computing in Civil Engineering.Seattle,Washington,USA,2017:298-306. [21]NAIR V,HINTON G E.Rectified linear units improve restricted boltzmann machines[C]∥Proceedings of the 27th International Conference on Machine Learning.Haifa,Israel,2010:807-814. [22]BOUREAU Y L,PONCE J,LECUN Y.A theoretical analysis of feature pooling in visual recognition[C]∥Proceedings of the 27th International Conference on Machine Learning.Haifa,Israel,2010:111-118. [23]SCHERER D,MÜLLER A,BEHNKE S.Evaluation of pooling operations in convolutional architectures for object recognition [C] //Proceedings of the ICANN.Thessaloniki,Greece,2010:92-101. [24]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting [J].The Journal of Machine Learning Research,2014,15(1):1929-1958. [25]WANG Y,HUANG M,ZHAO L.Attention-based lstmfor aspect-level sentiment classification[C]∥Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.2016:606-615. [26]BOUREAU Y L,PONCE J,LECUN Y.A theoretical analysis of feature pooling in visual recognition[C]∥Proceedings of the 27th International Conference on Machine Learning (ICML-10).2010:111-118. [27]SCHERER D,MüLLER A,BEHNKE S.Evaluation of pooling operations in convolutional architectures for object recognition[M]∥Artificial Neural Networks-ICANN 2010.Springer,Berlin,Heidelberg,2010:92-101 [28]BETTAHAR S,STAMBOULI A B,LAMBERT P,et al.PDE-based enhancement of color images in RGB space [J].IEEE Transactions on Image Processing,2012,21(5):2500-2512. [29]JIA Y,SHELHAMER E,DONAHUE J,et al.Caffe:convolutional architecture for fast feature embedding[C]∥Proceedings of the 22nd ACM International Conference on Multimedia.New York,USA,2014:675-678.

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