[1] RAWAT W, WANG Z H.Deep convolutional neural networks for image classification:a comprehensive review[J].Neural Computation, 2017, 29(9):2352-2449. [2] JADERBERG M, SIMONYAN K, VEDALDI A, et al.Deep structured output learning for unconstrained text recognition[EB/OL].[2020-11-15].http://arxiv.org/abs/1412.5903. [3] RUSSAKOVSKY O, DENG J, SU H, et al.ImageNet large scale visual recognition challenge[J].International Journal of Computer Vision, 2015, 115(3):211-252. [4] KRIZHEVSKY A, SUTSKEVER I, HINTON G E.ImageNet classification with deep convolutional neural networks[J].Communications of the ACM, 2017, 60(6):84-90. [5] HE K M, ZHANG X Y, REN S Q, 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, 2016:770-778. [6] PASZKE A, GROSS S, CHINTALA S, et al.Automatic differentiation in PyTorch[EB/OL].[2020-11-15].https://openreview.net/forum?id=BJJsrmfCZ. [7] LIU W, ANGUELOV D, ERHAN D, et al.SSD:single shot multibox detector[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2016:21-37. [8] 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. [9] LONG J, SHELHAMER E, DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2015:3431-3440. [10] NAM H, HAN B.Learning multi-domain convolutional neural networks for visual tracking[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:4293-4302. [11] SIMONYAN K, ZISSERMAN A.Two-stream convolutional networks for action recognition in videos[EB/OL].[2020-11-15].http://arxiv.org/pdf/1406.2199v2.pdf. [12] VAN DYK D A, MENG X L.The art of data augmentation[J].Journal of Computational and Graphical Statistics, 2001, 10(1):1-50. [13] HUANG G, SUN Y, LIU Z, et al.Deep networks with stochastic depth[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2016:646-661. [14] SZEGEDY C, VANHOUCKE V, IOFFE S, et al.Rethinking the inception architecture for computer vision[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:2818-2826. [15] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al.dropout:a simple way to prevent neural networks from overfitting[J].The Journal of Machine Learning Research, 2014, 15(1):1929-1958. [16] 赛迪顾问智能制造研究中心.中国工业机器视觉产业发展白皮书[J].机器人产业, 2020(6):76-95. CCID Consulting Intelligent Manufacturing Research Center.White paper on the development of China's industrial machine vision industry[J].Robot Industry, 2020(6):76-95.(in Chinese) [17] 王乐.基于深度学习机器视觉的分拣系统设计与实现[D].长沙:长沙理工大学, 2019. WANG L.Design and implementation of sorting system based on deep learning of machine vision[D].Changsha:Changsha University of Science & Technology, 2019.(in Chinese) [18] 张浩.液压制动器密封凹槽缺陷机器视觉检测系统[D].杭州:中国计量学院, 2015. ZHANG H.The detection system of hydraulic brake master cylinder surface defect based on machine vision[D].Hangzhou:China University of Metrology, 2015.(in Chinese) [19] 陈立潮, 闫耀东, 张睿, 等.融合迁移学习的AlexNet神经网络不锈钢焊缝缺陷分类[J].智能系统学报, 2021, 16(3):537-543. CHEN L C, YAN Y D, ZHANG R, et al.Welding defect classification of stainless steel based on AlexNet neural network combined with transfer learning[J].CAAI Transactions on Intelligent Systems, 2021, 16(3):537-543.(in Chinese) [20] 芦海利, 姚军, 王崇磊, 等.多源数据融合的低分辨率无人机图像增强处理[J].信息技术, 2020, 44(11):112-116. LU H L, YAO J, WANG C L, et al.Multi-source data fusion of low-resolution UAV image enhancement processing[J].Information Technology, 2020, 44(11):112-116.(in Chinese) [21] DEVRIES T, TAYLOR G W.Improved regularization of convolutional neural networks with Cutout[EB/OL].[2020-11-15].https://arxiv.org/abs/1708.04552. [22] YUN S, HAN D, CHUN S, et al.CutMix:regularization strategy to train strong classifiers with localizable features[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2019:6022-6031. [23] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al.Generative adversarial networks[J].Communications of the ACM, 2020, 63(11):139-144. [24] HINTON G, VINYALS O, DEAN J.Distilling the knowledge in a neural network[EB/OL].[2020-11-15].https://arxiv.org/abs/1503.02531. [25] PATHAK D, KRÄHENBÜHL P, DONAHUE J, et al.Context encoders:feature learning by inpainting[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:2536-2544. [26] LIU G, REDA F A, SHIH K J, et al.Image inpainting for irregular holes using partial convolutions[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2018:85-100. [27] ZHANG H, CISSE M, DAUPHIN Y N, et al.Mixup:beyond empirical risk minimization[EB/OL].[2020-11-15].https://arxiv.org/abs/1710.09412. [28] ZHONG Z, ZHENG L, KANG G, et al.Random erasing data augmentation[C]//Proceedings of 2020 AAAI Conference on Artificial Intelligence.Palo Alto, USA:AAAI Press, 2020:13001-13008. [29] LOSHCHILOV I, HUTTER F.SGDR:stochastic gradient descent with warm restarts[EB/OL].[2020-11-15].https://arxiv.org/abs/1608.03983. [30] CHEN L C, ZHU Y K, PAPANDREOU G, et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2018:801-818. [31] HENDRYCKS D, GIMPEL K.A baseline for detecting misclassified and out-of-distribution examples in neural networks[EB/OL].[2020-11-15].https://arxiv.org/abs/1610.02136. |