激光与光电子学进展, 2019, 56 (10): 101009, 网络出版: 2019-07-04   

基于卷积神经网络的短切毡缺陷分类 下载: 1009次

Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network
作者单位
西安工程大学电子信息学院, 陕西 西安 710048
引用该论文

卓东, 景军锋, 张缓缓, 苏泽斌. 基于卷积神经网络的短切毡缺陷分类[J]. 激光与光电子学进展, 2019, 56(10): 101009.

Dong Zhuo, Junfeng Jing, Huanhuan Zhang, Zebin Su. Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101009.

参考文献

[1] 邓和平, 薄圣泉. 玻璃纤维短切原丝毡中粘结剂和毡的强度关系[J]. 玻璃纤维, 2015( 2): 18- 21.

    Deng HP, Bo SQ. Correlation offiberglass chopped strand mat strength with its binder[J]. Fiber Glass, 2015( 2): 18- 21.

[2] 张君扬, 景军锋. 基于深度学习和分段线性插值的短切毡缺陷分类[J]. 西安工程大学学报, 2018, 32(5): 553-559.

    Zhang J Y, Jing J F. Defect classification of chopped strand mats based on deep learning and piecewise linear interpolation[J]. Journal of Xi'an Polytechnic University, 2018, 32(5): 553-559.

[3] SzegedyC, LiuW, Jia YQ, et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and PatternRecognition ( CVPR), 7-12June2015, Boston, MA, USA. New York: IEEE, 15523970.

[4] 杜剑, 胡炳樑, 张周锋. 基于卷积神经网络与显微高光谱的胃癌组织分类方法研究[J]. 光学学报, 2018, 38(6): 0617001.

    Du J, Hu B L, Zhang Z F. Gastriccarcinoma classification based on convolutional neural network and micro-hyperspectral imaging[J]. Acta Optica Sinica, 2018, 38(6): 0617001.

[5] 褚晶辉, 吴泽蕤, 吕卫, 等. 基于迁移学习和深度卷积神经网络的乳腺肿瘤诊断系统[J]. 激光与光电子学进展, 2018, 55(8): 081001.

    Chu J H, Wu Z R, Lü W, et al. Breast cancer diagnosis system based on transfer learning and deep convolutional neural networks[J]. Laser & Optoelectronics Progress, 2018, 55(8): 081001.

[6] Jing J F, Fan X T, Li P F. Patterned fabric defect detection via convolutional matching pursuit dual-dictionary[J]. Optical Engineering, 2016, 55(5): 053109.

[7] 方旭, 王光辉, 杨化超, 等. 结合均值漂移分割与全卷积神经网络的高分辨遥感影像分类[J]. 激光与光电子学进展, 2018, 55(2): 022802.

    Fang X, Wang G H, Yang H C, et al. High resolution remote sensing image classification combining with mean-shift segmentation and fully convolution neural network[J]. Laser & Optoelectronics Progress, 2018, 55(2): 022802.

[8] 来文豪, 周孟然, 王亚, 等. 深度学习与激光诱导荧光在假酒识别中的应用[J]. 激光与光电子学进展, 2018, 55(4): 043001.

    Lai W H, Zhou M R, Wang Y, et al. Application of counterfeit liquor recognition based on deep learning and laser induced fluorescence[J]. Laser & Optoelectronics Progress, 2018, 55(4): 043001.

[9] LeCun Y, Boser B, Denker J S, et al. . Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4): 541-551.

[10] YangW, Wanli OY, Li HS, et al. End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation[C]∥2016 IEEE Conference on Computer Vision and PatternRecognition ( CVPR), 27-30June2016, Las Vegas, NV, USA. New York: IEEE, 16541427.

[11] Zeiler MD, FergusR. Visualizing and understanding convolutional networks[M] ∥Zeiler M D, Fergus R. eds. Computer Vision-ECCV 2014. Cham: Springer International Publishing, 2014: 818- 833.

[12] KrizhevskyA, SutskeverI, Hinton GE. ImageNet classification with deep convolutional neural networks[C]∥International Conference on Neural Information Processing Systems. Dickinson: Curran Associates Inc.2012: 1097- 1105.

[13] Hearst M A, Dumais S T, Osuna E, et al. Support vector machines[J]. IEEE Intelligent Systems and their Applications, 1998, 13(4): 18-28.

[14] OquabM, BottouL, LaptevI, et al. Learning and transferring mid-level image representations using convolutional neural networks[C]∥2014 IEEE Conference on Computer Vision and PatternRecognition, 23-28June2014, Columbus, OH, USA. New York: IEEE, 14632209.

[15] Donahue J, Hendricks L A, Rohrbach M, et al. Long-term recurrent convolutional networks for visual recognition and description[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 677-691.

[16] Wang Z, Crammer K, Vucetic S. Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training[J]. Journal of Machine Learning Research, 2012, 13(1): 3103-3131.

卓东, 景军锋, 张缓缓, 苏泽斌. 基于卷积神经网络的短切毡缺陷分类[J]. 激光与光电子学进展, 2019, 56(10): 101009. Dong Zhuo, Junfeng Jing, Huanhuan Zhang, Zebin Su. Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101009.

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