复杂背景下基于图像处理的桥梁裂缝检测算法
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摘要
针对传统桥梁裂缝检测算法不能准确提取裂缝的问题,提出了一种复杂背景下基于图像处理的桥梁裂缝检测算法。根据深度卷积生成式对抗网络原理,利用桥梁裂缝图像生成模型,对数据集进行扩增。针对裂缝特征构建基于语义分割的桥梁裂缝图像分割模型,利用桥梁裂缝图像分割模型提取高分辨率裂缝图像中的裂缝。研究结果表明,与现有算法相比,所提算法在复杂道路场景中具有更好的检测效果和更强的泛化能力。
In order to solve the problem that the traditional bridge crack detection algorithm cannot extract cracks accurately,a bridge crack detection algorithm is proposed based on image processing,which is suitable for complex scenes.According to the principle of the deep convolutional generative adversarial network,the bridge crack image generative model is proposed and used to amplify the dataset.For the characteristics of bridge cracks,a bridge crack image segmentation model is constructed based on semantic segmentation.The bridge crack image segmentation model is used to extract the bridge cracks from the high-resolution crack images.The research results show that the proposed algorithm has a better detection effect and a stronger generalization ability in the complex road scenes compared with the existing algorithms.
In order to solve the problem that the traditional bridge crack detection algorithm cannot extract cracks accurately,a bridge crack detection algorithm is proposed based on image processing,which is suitable for complex scenes.According to the principle of the deep convolutional generative adversarial network,the bridge crack image generative model is proposed and used to amplify the dataset.For the characteristics of bridge cracks,a bridge crack image segmentation model is constructed based on semantic segmentation.The bridge crack image segmentation model is used to extract the bridge cracks from the high-resolution crack images.The research results show that the proposed algorithm has a better detection effect and a stronger generalization ability in the complex road scenes compared with the existing algorithms.
引文
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[2]Qu L,Wang K R,Chen L L,et al.Fast road detection based on RGBD images and convolutional neural network[J].Acta Optica Sinica,2017,37(10):1010003.曲磊,王康如,陈利利,等.基于RGBD图像和卷积神经网络的快速道路检测[J].光学学报,2017,37(10):1010003.
[3]National Bureau of Statistics of People's Republic of China.The statistics communique on national economy and social development of China 2017[N/OL].China Information News,2018-02-28(003).https:∥baijiahao.baidu.com/s?id=1593656271082733578&wfr=spider&for=pc.中华人民共和国国家统计局.中华人民共和国2017年国民经济和社会发展统计公报[N/OL].中国信息报,2018-02-28(003).https:∥baijiahao.baidu.com/s?id=1593656271082733578&wfr=spider&for=pc.
[4]Deng X L,Tian S Z.Bridge deformation detection and data processing based on 3Dlaser scanning[J].Laser&Optoelectronics Progress,2018,55(7):071201.邓晓隆,田石柱.基于三维激光扫描的桥梁变形检测及数据处理[J].激光与光电子学进展,2018,55(7):071201.
[5]Wang B,Wang X,Chen F,et al.Pavement crack recognition based on aerial image[J].Acta Optica Sinica,2017,37(8):0810004.王博,王霞,陈飞,等.航拍图像的路面裂缝识别[J].光学学报,2017,37(8):0810004.
[6]Oh H,Garrick N W,Achenie L.Segmentation algorithm using iterative clipping for processing noisy pavement images[C]∥Imaging Technologies:2nd International Conference on Techniques And Application In Civil Engineering,May 25-30,1997,Davos,Switzerland.Washington:TRB Publications,1998:138-147.
[7]Sun L,Xing J C,Xie L Q,et al.An adaptive threshold-based Canny algorithm for crack detection[J].Microcomputer&Its Applications,2017,36(5):35-37,41.孙亮,邢建春,谢立强,等.基于自适应阈值Canny算法的裂缝检测方法研究[J].微型机与应用,2017,36(5):35-37,41.
[8]Talab A M A,Huang Z C,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]Zhang L,Yang F,Zhang Y D,et al.Road crack detection using deep convolutional neural network[C]∥IEEE International Conference on Image Processing,September 25-28,2016,Phoenix,AZ,USA.New York:IEEE,2016:3708-3712.
[10]Chen F C,Jahanshahi M R.NB-CNN:Deep learning-based crack detection using convolutional neural network and na6ve bayes data fusion[J].IEEETransactions on Industrial Electronics,2018,65(5):4392-4400.
[11]Shi Y,Cui L M,Qi Z Q,et al.Automatic road crack detection using random structured forests[J].IEEE Transactions on Intelligent Transportation Systems,2016,17(12):3434-3445.
[12]Radford A,Metz L,Chintala S.Unsupervised representation learning with deep convolutional generative adversarial networks[C]∥International Conference on Learning Representations 2016(ICLR 2016),May 2-4,2016,San Juan,Puerto Rico.New York:Cornell University Library,2016:1511.06434.
[13]Klambauer G,Unterthiner T,Mayr A,et al.SelfNormalizing neural networks[C]∥Neural Information Processing Systems 2017,December 4-9,2017,Long Beach,CA,USA.New York:Cornell University Library,2016:1706.02515.
[14]Li F X,Carreira J,Lebanon G,et al.Composite statistical inference for semantic segmentation[C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition,June 23-28,2013,Portland,OR,USA.New York:IEEE,2013:3302-3309.
[15]Guo C C,Yu F Q,Chen Y.Image semantic segmentation based on convolutional neural network feature and improved superpixel matching[J].Laser&Optoelectronics Progress,2018,55(8):081005.郭呈呈,于凤芹,陈莹.基于卷积神经网络特征和改进超像素匹配的图像语义分割[J].激光与光电子学进展,2018,55(8):081005.
[16]Badrinarayanan V,Kendall A,Cipolla R.SegNet:Adeep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495.
[17]Shelhamer E,Long J,Darrell T.Fully convolutional networks for semantic segmentation[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2017,39(4):640-651.
[18]Chen L C,Papandreou G,Kokkinos I,et al.DeepLab:semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(4):834-848.
[19]Huang G,Liu Z,Maaten L V D,et al.Densely connected convolutional networks[C]∥IEEEConference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:2261-2269.
[20]Jégou S,Drozdzal M,Vazquez D,et al.The one hundred layers tiramisu:Fully convolutional DenseNets for semantic segmentation[C]∥IEEEConference on Computer Vision and Pattern Recognition Workshops,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:1175-1183.
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