Research on the Application of Convolutional Neural Networks in the Image Recognition

[1]Hinton G E, Salakhutdinov R R, Reducing the dimensionality of data with neural networks[J].Science, 2006,313(5786):504–507.HintonG ESalakhutdinovR RReducing the dimensionality of data with neural networks[J]Science2006313578650450710.1126/science.112764716873662Search in Google Scholar

[2]Rumelhart D E, Hinton G E, Williams RJ. Learning representations by back-propagating errors[J].Nature, 1986, 323:533–538.RumelhartD EHintonG EWilliamsRJLearning representations by back-propagating errors[J]Nature198632353353810.1038/323533a0Search in Google Scholar

[3]Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, november 1998.Y.LeCunL.BottouY.Bengio, and P.HaffnerGradient-based learning applied to document recognitionProceedings of the IEEEnovember 199810.1109/5.726791Search in Google Scholar

[4]LeCun Y, Cortea C. MNIST handwritten digit database[EB/OL].http://yann.lecun.com/exdb/mnist, 2010.LeCunYCorteaC.MNIST handwritten digit database[EB/OL]http://yann.lecun.com/exdb/mnist2010Search in Google Scholar

[5]K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition.K.Simonyan and A.ZissermanVery Deep Convolutional Networks for Large-Scale Image RecognitionSearch in Google Scholar

[6]He K, Zhang x, Ren S, et al. Deep residual learning for image recognition[EB/OL].http://arxiv.org/abs/1512.03385,2015.HeKZhangxRenSDeep residual learning for image recognition[EB/OL]http://arxiv.org/abs/1512.033852015Search in Google Scholar

[7]Gu Jiu-Xiang, Wang Zhen-Hua, Jason Kuen, et al Recentadvances in convolutional neural networks. arXiv:1512.07180v5,2017.GuJiu-XiangWangZhen-HuaJasonKuenRecentadvances in convolutional neural networksarXiv:1512.07180v52017Search in Google Scholar

[8]X. Glorot, Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. AISTATS, 2010X.GlorotY.BengioUnderstanding the difficulty of training deep feedforward neural networksAISTATS2010Search in Google Scholar

[9]Lecun Y, Boser B, Denker J S, et al. Back propagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4):541–551.LecunYBoserBDenkerJ SBack propagation applied to handwritten zip code recognition[J]Neural Computation19891454155110.1162/neco.1989.1.4.541Search in Google Scholar

[10]Krizhevsky A, Sutskever I, Hinton G E. Imagenet classifi-cation with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012:1097–1105.KrizhevskyASutskeverIHintonG EImagenet classifi-cation with deep convolutional neural networks[C]Advances in Neural Information Processing Systems201210971105Search in Google Scholar

[11]Bengio Y, Simard P, Frasconi P. Learning long-term depen-dencies with gradient descent is difficult[J]. IEEE Transac-tions on Neural Networks, 1994, 5(2):157–166.BengioYSimardPFrasconiP.Learning long-term depen-dencies with gradient descent is difficult[J]IEEE Transac-tions on Neural Networks19945215716610.1109/72.27918118267787Search in Google Scholar

[12]Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, Going Deeper with Convolutions, Arxiv Link: http://arxiv.org/abs/1409.4842.ChristianSzegedyWeiLiuYangqingJiaPierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, Going Deeper with ConvolutionsArxiv Link: http://arxiv.org/abs/1409.4842Search in Google Scholar

[13]Rougui J E, Istrate D, Souidene W, et al. Audio Based Sur-veillance for Cognitive Assitance Using a CMT Microphonewithin Socially Assistive Living [J]. 2009, 2009 (2009):2547–2550.]RouguiJ EIstrateDSouideneWAudio Based Sur-veillance for Cognitive Assitance Using a CMT Microphonewithin Socially Assistive Living [J]2009200920092547255010.1109/IEMBS.2009.533476219964978Search in Google Scholar

[14]Lin, Min, Qiang Chen, and Shuicheng Yan. Network in network. arXiv preprint arXiv:1312.4400LinMinQiangChen, and ShuichengYanNetwork in networkarXiv preprint arXiv:1312.4400Search in Google Scholar

[15]Zang D, Chai Z, Zhang J, et al. Vehicle license plate recog-nition using visual attention model and deep learning[J]. Journal of Electronic Imaging, 2015, 24(3):033001.ZangDChaiZZhangJVehicle license plate recog-nition using visual attention model and deep learning[J]Journal of Electronic Imaging201524303300110.1117/1.JEI.24.3.033001Search in Google Scholar

[16]Xv Shanshan. Research and application of convolutional neural network [D]. Nanjing forestry university, 2013.XvShanshanResearch and application of convolutional neural network [D]Nanjing forestry university2013Search in Google Scholar

[17]Hinton G E, Srivastava N, Krizhevsky A, et al Improving neural networks by preventing co-adaption of feature detectors[ Ｒ /OL].[2015-10-26]. http://arxiv.org/pdf/12070580v1HintonG ESrivastavaNKrizhevskyAImproving neural networks by preventing co-adaption of feature detectors[ Ｒ /OL].[2015-10-26]http://arxiv.org/pdf/12070580v1Search in Google Scholar

[18]Wan L, Zeiler M, Zhang S, et al. Regularization of neural networks using drop connect [C] / / Proceedings of the 2013 Inter-national Conference on Machine Learning. New York: ACM Press, 2013: 1058－1066.WanLZeilerMZhangSRegularization of neural networks using drop connect [C]Proceedings of the 2013 Inter-national Conference on Machine LearningNew YorkACM Press201310581066Search in Google Scholar

[19]Luo Ke, Zhou Anzhong, Luo Xiao. A Convolutional neural network training strategy using knowledge transfer[J]. Control and decision-making, 2019, 034 (003) : 511-518.LuoKeZhouAnzhongLuoXiaoA Convolutional neural network training strategy using knowledge transfer[J]Control and decision-making2019034003511518Search in Google Scholar

[20]Cao Ge. Research on face image classification application based on deep convolutional neural network [D]. Jilin university, 2019.CaoGeResearch on face image classification application based on deep convolutional neural network [D]Jilin university2019Search in Google Scholar

[21]Shi Wenxu, Jiang Jinhong, Bao Shengli. Application of improved convolutional neural network in pathological image classification of adenocarcinoma[J]. Science, technology and engineering, 209,19 (35) : 279-285.ShiWenxuJiangJinhongBaoShengliApplication of improved convolutional neural network in pathological image classification of adenocarcinoma[J]Science, technology and engineering2091935279285Search in Google Scholar

[22]Zhang Chun-lei. Military target image classification technology based on parallel convolutional neural network[J]. Electronic design engineering, 209, 27 (08) : 76-80.ZhangChun-leiMilitary target image classification technology based on parallel convolutional neural network[J]Electronic design engineering20927087680Search in Google Scholar

[23]Rao Jianghao. Target identification, positioning and detection based on CNN integration [D]. University of Chinese academy of sciences (institute of optoelectronic technology, Chinese academy of sciences), 2018.RaoJianghaoTarget identification, positioning and detection based on CNN integration [D]University of Chinese academy of sciences (institute of optoelectronic technology, Chinese academy of sciences)2018Search in Google Scholar

[24]Li Dajun, He Weilong, Guo Bingxuan, Li Maosen, Chen Minqiang. Building target detection algorithm based on Mask - RCNN[J]. Science of surveying and mapping, 202,44 (10) : 172-180.LiDajunHeWeilongGuoBingxuanLiMaosenChenMinqiangBuilding target detection algorithm based on Mask - RCNN[J]Science of surveying and mapping2024410172180Search in Google Scholar

[25]DingPeng. Research on optical remote sensing target detection technology based on deep convolutional neural network [D]. University of Chinese academy of sciences (changchun institute of optics, precision mechanics and physics, cas), 2019.DingPengResearch on optical remote sensing target detection technology based on deep convolutional neural network [D]University of Chinese academy of sciences (changchun institute of optics, precision mechanics and physics, cas)2019Search in Google Scholar

[26]Zhang Qi, Ding Xintao, Wang Wanjun, SZhou Wen. Traffic target detection method based on Faster RCNN [J]. Journal of west anhui university, 202,35 (05) : 50-55.ZhangQiDingXintaoWangWanjunSZhouWenTraffic target detection method based on Faster RCNN [J]Journal of west anhui university20235055055Search in Google Scholar