Research on the Tunnel Geological Radar Image Flaw Detection Based on CNN

[1]Li Wendi. Analysis of GPR image features of tunnel lining defects. [J]. Fujian Building Materials, 2019(01):22-24.LiWendiAnalysis of GPR image features of tunnel lining defects. [J]Fujian Building Materials2019012224Search in Google Scholar

[2]Zhang Chi. Research on detection of lining voids of reinforced concrete structures based on geological radar method [J]. Railway survey, 2018, 44 (03):35-38.ZhangChiResearch on detection of lining voids of reinforced concrete structures based on geological radar method [J]Railway survey201844033538Search in Google Scholar

[3]Liu Jinlong, Tan hailiang. Application of geological radar in detecting soil defects [J]. Engineering quality, 2018, 36 (01):73-75.LiuJinlongTanhailiangApplication of geological radar in detecting soil defects [J]Engineering quality201836017375Search in Google Scholar

[4]Hinton G E, Osindero S, Teh Y. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18: 1527-1554.HintonG EOsinderoSTehYA fast learning algorithm for deep belief nets [J]Neural Computation2006181527155410.1162/neco.2006.18.7.152716764513Search in Google Scholar

[5]Krizhevsky A, Sutskeever I, Hinton G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the Acm, 2012, 60(2): 1097-1105.KrizhevskyASutskeeverIHintonG EImageNet classification with deep convolutional neural networks [J]Communications of the Acm201260210971105Search in Google Scholar

[6]Girshick R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1440-1448.GirshickR.Fast R-CNN[C]2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, ChileNew YorkIEEE20151440144810.1109/ICCV.2015.169Search in Google Scholar

[7]Ren S, He K, Girshick R, et al. Faster R-CNN: towards real time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.RenSHeKGirshickRFaster R-CNN: towards real time object detection with region proposal networks[J]IEEE Transactions on Pattern Analysis and Machine Intelligence20173961137114910.1109/TPAMI.2016.257703127295650Search in Google Scholar

[8]Sun Y, Liang D, Wang X G, et al. DeepID3: face recognition with very deep neural networks [J]. Computer Science, 2015, 2(3): 1-5.SunYLiangDWangX GDeepID3: face recognition with very deep neural networks [J]Computer Science20152315Search in Google Scholar

[9]Luiz G H, Robert S, Luiz S O. Written dependent feature learning for offine signature verification using deep convolutional neutral networks [J]. Pattern Recognition, 2017(70): 163-176.LuizG HRobertSLuizS OWritten dependent feature learning for offine signature verification using deep convolutional neutral networks [J]Pattern Recognition20177016317610.1016/j.patcog.2017.05.012Search in Google Scholar

[10]Abdelhamid O, Mohamed A R, Jiang H, et al. Convolutional neural networks for speech recognition[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014, 22(10): 1533-1545.AbdelhamidOMohamedA RJiangHConvolutional neural networks for speech recognition[J]IEEE/ACM Transactions on Audio, Speech, and Language Processing201422101533154510.1109/TASLP.2014.2339736Search in Google Scholar

[11]Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Seattle, WA, USA. New York: IEEE, 779-788.RedmonJDivvalaSGirshickRYou only look once: unified, real-time object detection[C]Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Seattle, WA, USANew YorkIEEE77978810.1109/CVPR.2016.91Search in Google Scholar

[12]Du Yuhong, Dong Chao-qun, etc. Application of improved Faster RCNN model in cotton fiber identification [J/OL]. Advances in laser and optoelectronics: 1-14 [2019-11-25].DuYuhongDongChao-qunetcApplication of improved Faster RCNN model in cotton fiber identification [J/OL]Advances in laser and optoelectronics114[2019-11-25]Search in Google Scholar

[13]Xu shoukun, Wang yaru, Gu yuwan etc. Research on the detection of helmet wearing based on improved FasterRCNN [J/OL]. Computer application research: 1-6 [2019-11-25].XushoukunWangyaruGuyuwanetcResearch on the detection of helmet wearing based on improved FasterRCNN [J/OL]Computer application research16[2019-11-25]Search in Google Scholar

[14]Song Shang-ling, Yang Yang etc. Pulmonary nodules detection algorithm based on Faster-RCNN [J/OL]. Chinese journal of biomedical engineering: 1-8 [2019-11-25].SongShang-lingYangYangetcPulmonary nodules detection algorithm based on Faster-RCNN [J/OL]Chinese journal of biomedical engineering18[2019-11-25]Search in Google Scholar

[15]Wang J, Chen K, Yang S, et al. Region Proposal by Guided Anchoring [J]. 2019. Rezatofighi H, Tsoi N, Gwak J Y, et al. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression [J]. 2019.WangJChenKYangSRegion Proposal by Guided Anchoring [J]2019Rezatofighi H, Tsoi N, Gwak J Y, et al. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression [J]. 2019Search in Google Scholar

[16]Rezatofighi H, Tsoi N, Gwak J Y, etc. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression[J]. 2019.RezatofighiHTsoiNGwakJ YetcGeneralized Intersection over Union: A Metric and A Loss for Bounding Box Regression[J]201910.1109/CVPR.2019.00075Search in Google Scholar

[17]Zheng Lifei, Xiao lito, Li Xiaoqing. Forward modeling and application of geological radar advance prediction [J]. Communications science and technology, 2018(2):76-81.ZhengLifeiXiaolitoLiXiaoqingForward modeling and application of geological radar advance prediction [J]Communications science and technology201827681Search in Google Scholar

[18]Wu Zhengwen. Application of convolution neural network in image classification. Chengdu: University of Electronic Science and technology of China, 2015WuZhengwenApplication of convolution neural network in image classificationChengduUniversity of Electronic Science and technology of China2015Search in Google Scholar

[19]Lin Gang, Wang Bo etc. Multi-target detection and positioning of power line inspection image based on improved faster-RCNN [J]. Power automation equipment, 2019, 39(05):213-218.LinGangWangBoetcMulti-target detection and positioning of power line inspection image based on improved faster-RCNN [J]Power automation equipment20193905213218Search in Google Scholar

[20]Sainath TN, Kingsbury B, Saon G, et al. Deep convolutional neural networks for large-scale speech tasks [J]. Neural Networks, 2015, 64:39-48.SainathTNKingsburyBSaonGDeep convolutional neural networks for large-scale speech tasks [J]Neural Networks201564394810.1016/j.neunet.2014.08.00525439765Search in Google Scholar