[1] MOHAN A R,POOBAL S.Crack detection using image processing:a critical review and analysis[J].Alexandria Engineering Journal,2018,57(2):787-798. [2] DAVIS L S.A survey of edge detection techniques[J].Computer Graphics and Image Processing,1975,4(3):248-270. [3] HAN Siqi,WANG Lei.A survey of thresholding methods for image segmentation[J].Journal of Systems Engineering and Electronics,2002,24(6):91-94.(in Chinese)韩思奇,王蕾.图像分割的阈值法综述[J].系统工程与电子技术,2002,24(6):91-94. [4] ADHIKARI R S,MOSELHI O,BAGCHI A.Image-based retrieval of concrete crack properties for bridge inspection[J].Automation in Construction,2014,39(4):180-194. [5] CHA Y J,CHOI W,BÜYÜKÖZTÜRK O.Deep learning-based crack damage detection using convolutional neural networks[J].Computer-Aided Civil and Infrastructure Engineering,2017,32(5):361-378. [6] KIM B,CHO S.Automated vision-based detection of cracks on concrete surfaces using a deep learning technique[J].Sensors,2018,18(10):3452-3453. [7] WEN Zuolin,SHEN Yonggang,SU Jian,et al.Concrete crack detection method based on CNN[J].Low Tem-perature Architecture Technology,2019,41(6):9-12.(in Chinese)温作林,申永刚,苏建,等.基于卷积神经网络的混凝土裂缝识别[J].低温建筑技术,2019,41(6):9-12. [8] HAN Xiaojian,ZHAO Zhicheng.Structural surface crack detection method based on computer vision technology[J].Journal of Building Structures,2018,39(S1):418-427.(in Chinese)韩晓健,赵志成.基于计算机视觉技术的结构表面裂缝检测方法研究[J].建筑结构学报,2018,39(S1):418-427. [9] LIU Xingen,CHEN Yingying,ZHU Aixi,et al.Tunnel crack identification based on deep learning[J].Journal of Guangxi University(Natural Science Edition),2018,43(6):2243-2251.(in Chinese)刘新根,陈莹莹,朱爱玺,等.基于深度学习的隧道裂缝识别方法[J].广西大学学报(自然科学版),2018,43(6):2243-2251. [10] FENG Hui.Research and implementation of road crack detection algorithm based on deep learning[D].Beijing:Beijing University of Posts and Telecommunications,2019.(in Chinese)冯卉.基于深度学习的道路裂缝识别算法研究与实现[D].北京:北京邮电大学,2019. [11] JIA Xiaoyu.Measurement based on convolutional neural networks[D].Liuzhou:Guangxi University of Science and Technology,2019.(in Chinese)贾潇宇.基于卷积神经网络的桥梁裂缝识别与测量方法研究[D].柳州:广西科技大学,2019. [12] SHELHAMER E,LONG J,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4):640-651. [13] LI Shengyuan,ZHAO Xuefeng,ZHOU Guangyi.Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network[J].Computer-Aided Civil and Infrastructure Engineering,2019,34(7):616-634. [14] YANG Xincong,LI Heng,YU Yantao,et al.Automatic pixel-level crack detection and measurement using fully convolutional network[J].Computer-Aided Civil and Infrastructure Engineering,2018,33(12):1090-1109. [15] WANG Sen,WU Xing,ZHANG Yinhui,et al.Image crack detection with fully convolutional network based on deep learning[J].Journal of Computer-Aided Design and Computer Graphics,2018,30(5):859-867.(in Chinese)王森,伍星,张印辉,等.基于深度学习的全卷积网络图像裂纹检测[J].计算机辅助设计与图形学学报,2018,30(5):859-867. [16] LI Chuanpeng,QIN Pinle,ZHANG Jinjing.Research on image denoising based on deep convolutional neural network[J].Computer Engineering,2017,43(3):253-260.(in Chinese)李传朋,秦品乐,张晋京.基于深度卷积神经网络的图像去噪研究[J].计算机工程,2017,43(3):253-260. [17] BADRINARAYANAN V,KENDALL A,CIPOLLA R.SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495. [18] NOH H,HONG S,HAN B.Learning deconvolution network for semantic segmentation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:21-27. [19] RONNEBERGER O,FISCHER P,BROX T.U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of 2015 International Conference on Medical Image Computing and Computer-assisted Intervention.Berlin,Germany:Springer,2015:234-241. [20] CHENG Jierong,XIONG Wei,CHEN Wenyu,et al.Pixel-level crack detection using U-Net[C]//Proceedings of TENCON'18.Washington D.C.,USA:IEEE Press,2018:462-466. [21] ZHU Suya,DU Jianchao,LI Yunsong,et al.Method for bridge crack detection based on the U-Net convolutional networks[J].Journal of Xidian University,2019,46(6):1-8.(in Chinese)朱苏雅,杜建超,李云松,等.采用-卷积网络的桥梁裂缝检测方法[J].西安电子科技大学学报,2019,46(6):1-8. [22] ROY A G,NAVAB N,WACHINGER C.Concurrent spatial and channel ‘squeeze & excitation’ in fully con-volutional networks[C]//Proceedings of MICCAI'18.Berlin,Germany:Springer 2018:421-429. [23] WONG Piao.Research on segmentation algorithm of pavement crack in complex environment[D].Zhengzhou:Zhengzhou University,2019.(in Chinese)翁飘.复杂环境下路面裂缝分割算法研究[D].郑州:郑州大学,2019. [24] HARIHARAN B,ARBELAEZ P,GIRSHICK R,et al.Hypercolumns for object segmentation and fine-grained localization[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:33-39. [25] KANG Le,YE Peng,LI Yi,et al.Convolutional neural networks for no-reference image quality assessment[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:55-62. [26] HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al.Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:132-138. [27] DORAFSHAN S,MAGUIRE M,THOMAS R.SDNET2018:a concrete crack image dataset for machine learning applications[EB/OL].[2019-08-03].https://www.researchgate.net/publication/325719429_SDNET2018_A_concrete_crack_image_dataset_for_machine_learning_applications. |