Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (3): 267-276.doi: 10.11896/j.issn.1002-137X.2019.03.040

• Artificial Intelligence • Previous Articles     Next Articles

Dam Defect Recognition and Classification Based on Feature Combination and CNN

MAO Ying-chi1,WANG Jing1,CHEN Xiao-li1,XU Shu-fang1,CHEN Hao2   

  1. (College of Computer and Information,Hohai University,Nanjing 211100,China)1
    (College of Water Conservancy and Hydropower,Hohai University,Nanjing 210098,China)2
  • Received:2018-07-03 Revised:2018-09-15 Online:2019-03-15 Published:2019-03-22

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Abstract: Dam defect recognition and classification technology is the basic manifestation of human intelligence.It is one of the most typical and difficult pattern recognition problems.Due to the low signal-to-noise ratio and extremely uneven illumination distribution of dam defects,the recognition rate of classification and recognition algorithm is relatively low.In order to solve these problems,this paper proposed a defect image recognition method based on the combination of ima-ge LBP features and image Gabor features combined with CNN(LBP and Gabor feature combination and CNN,LG-CNN),analyzed the collected dam image,and realized the recognition and classification of the defective images.Firstly,the LBP features and the Gabor features of images are extracted respectively.Then,the features of LBP and Gabor are combined to be the input of CNN.Finally,by training the network layer by layer,the classification and recognition of dam defects are realized.The experimental results show that the average recognition accuracy of LGk-CNN is 88.39%,as well as the recall rate of defect is 92.75%.Compared with the CNN classification algorithm under the same parameters,the recognition accuracy and therecall rate of defect are increased by 3.1% and 2.5% respectively,and the results is the best results.

Key words: Classification, CNN, Defect image, Gabor feature, LBP feature

CLC Number: 

  • TP311
[1]苏南.我国200米级高坝密集,安全风险不容忽视.http://www.thepaper.cn/newsDetail_forward_1858088.2017.
[2]ZHANG S B,LIU N,HUO H,et al.Image classification based on hierarchical shape feature extraction model .High-tech communication,2016,26 (1):81-88.(in Chinese)
张盛博,刘娜,霍宏,等.基于层次形状特征提取模型的图像分类.高技术通讯,2016,26(1):81-88.
[3]DENG H B,JIN L W.Facial Expression Recognition Based on Local Gabor Filter Bank and PCA+LDA.Journal of Image &Graphics,2007,12(2):322-329.(in Chinese)
邓洪波,金连文.一种基于局部Gabor滤波器组及PCA+LDA的人脸表情识别方法.中国图象图形学报,2007,12(2):322-329.
[4]BELARBI M A,MAHMOUDI S,BELALEM G.PCA as Di-
mensionality Reduction for Large-Scale Image Retrieval Systems.International Journal of Ambient Computing and Intelligence (IJACI),2017,8(4):45-58.
[5]ZHANG R,WANG Q,ZHANG P.Image Texture Feature Extraction Based on Gabor Transform.Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia,2016,39(1):237-244.
[6]RAHMAN S,RAHMAN M M,HUSSAIN K,et al.Image enhancement in spatial domain:A comprehensive study∥2014 17th International Conference on Computer and Information Technology (ICCIT).IEEE,2014:368-373.
[7]XIE F,CHEN L T,QIU H.Research on image classification method based on texture feature extraction and system implementation.Computer Application Research,2009,26(7):2767-2770.(in Chinese)
谢菲,陈雷霆,邱航.基于纹理特征提取的图像分类方法研究及系统实现.计算机应用研究,2009,26(7):2767-2770.
[8]ZARNDY ,REKECZKY C,SZOLGAY P,et al.Overview of CNN research:25 years history and the current trends∥2015 IEEE International Symposium on Circuits and Systems (ISCAS).IEEE,2015:401-404.
[9]ZHAO J,TONG W Q.Near infrared face recognition based on LBP and Gabor hybrid features.Journal of East China Normal University (Natural Science Edition),2016(4):77-85.(in Chinese)
赵骥,童卫青.基于 LBP 和 Gabor 混合特征的近红外人脸识别.华东师范大学学报 (自然科学版),2016(4):77-85.
[10]JIANG Z,WU Q,TAN Y,et al.Analysis Range of Coefficients in Learning Rate Methods of Convolution Neural Network∥International Symposium on Distributed Computing & Applications for Business Engineering & Science.2016.
[11]GAO Y ,MA J ,ZHAO M ,et al.NDDR-CNN:Layer-wise Feature Fusing in Multi-Task CNN by Neural Discriminative Dimensionality Reduction.https://arxiv.org/pdf/1801.08297.pdf.
[12]TAN W X,ZHAO C,WU H,et al.Intelligent alerting for fruit-melon lesion image based on momentum deep learning.Multimedia Tools & Applications,2016,75(24):1-21.
[13]PENG C,XIAO T,LI Z,et al.MegDet:A Large Mini-Batch Object Detector∥IEEE Conference on Computer Vision and Pattern Recognition.2018:6181-6189.
[14]ELLEUCH M,MAALEJ R,KHERALLAH M.A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition.Procedia Computer Science,2016,80:1712-1723.
[15]LIU W,WEN Y,YU Z,et al.Large-margin softmax loss for convolutional neural networks∥International Conference on International Conference on Machine Learning.2016.
[16]NIE X,CHENG A,SHEN D,et al.New Algorithm SGD of Surface Modification and Its Application in Springback Compensation.China Mechanical Engineering,2008,24(19):2972-2976.
[17]CHERKASSKY V,MA Y.Practical selection of SVM parameters and noise estimation for SVM regression.Neural Networks,2004,17(1):113-126.
[18]ZHANG Y,ZHOU X,WITT R M,et al.Dendritic spine detection using curvilinear structure detector and LDA classifier.Neuroimage,2007,36(2):346-360.
[19]RTSCH G,ONODA T,MLLER K R.Soft Margins for AdaBoost.Machine Learning,2001,42(3):287-320.
[20]CHEN S,COWAN C N,GRANT P M.Orthogonal least squares learning algorithm for radial basis function networks.IEEE Transactions on Neural Netw,1991,64(5):829-837.
[21]STOEAN C.In Search of the Optimal Set of Indicators when Classifying Histopathological Images∥International Symposium on Symbolic & Numeric Algorithms for Scientific Computing.2017.
[22]BAG S,HARIT G.A medial axis based thinning strategy and structural feature extraction of character images∥IEEE International Conference on Image Processing.2010.
[23]FAN X,WU J,SHI P,et al.A novel automatic dam crack detection algorithm based on local-global clustering.Multimedia Tools & Applications,2018,77(20):26581-26599.
[24]KAWASAKI Y,MATSUSHIMA K,ZHONG Z.Image-based
pavement crack detection by percolation theory∥2017 9th International Conference onInformation Technology and Electrical Engineering (ICITEE).IEEE,2017:1-6.
[25]YU N,QIU T,FENG B,et al.Image Features Extraction and Fusion Based on Joint Sparse Representation.IEEE Journal of Selected Topics in Signal Processing,2011,5(5):1074-1082.
[26]WANG Z,CUI P,LI F,et al.A data-driven study of image feature extraction and fusion.Information Sciences,2014,281(4):536-558.
[27]TYCHSEN-SMITH L,PETERSSON L.DeNet:Scalable Real-time Object Detection with Directed Sparse Sampling∥2017 IEEE International Conference on Computer Vision(ICCV).IEEE Computer Society,2017.
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