基于优化卷积神经网络的表面缺陷检测
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摘要
卷积神经网络(CNN)具有权值数量少,训练速度快等优点,在图像识别、机器视觉等领域得到广泛应用。本文提出了一种卷积神经网络的自适应加权池化算法,算法通过生成合并通道,并在学习掩模的引导下汇集特征,优化了子采样模型的特征提取,有效改善了网络的识别准确性和快速性。利用该算法对磁片表面缺陷进行检测实验,实验结果表明,本文提出的池化模型使卷积神经网络对特征的提取更加精确,同时提高了收敛速度和鲁棒性,并且可以应用于各种深度神经网络体系结构中。
The convolutional neural networks(CNN) have been proved to be effective in image recognition, machine vision and other fields with the advantages of small number of weights and fast training speed. An adaptive weighted pooling algorithm is proposed for convolutional neural networks. The proposed algorithm optimizes the feature extraction of sub-sampling models by generating merge channels and collecting features under the guidance of learning masks, and effectively improves the recognition accuracy and speed of the network. The experiments carried out on the surface defect detection of the magnetic disks show that the proposed pooling model can improve the accuracy of the features extraction and so can effectively detect the defects of surface in faster convergence and robustness with convolutional neural network.Also the pooling model proposed in this paper can be applied to various deep neural network architectures.
The convolutional neural networks(CNN) have been proved to be effective in image recognition, machine vision and other fields with the advantages of small number of weights and fast training speed. An adaptive weighted pooling algorithm is proposed for convolutional neural networks. The proposed algorithm optimizes the feature extraction of sub-sampling models by generating merge channels and collecting features under the guidance of learning masks, and effectively improves the recognition accuracy and speed of the network. The experiments carried out on the surface defect detection of the magnetic disks show that the proposed pooling model can improve the accuracy of the features extraction and so can effectively detect the defects of surface in faster convergence and robustness with convolutional neural network.Also the pooling model proposed in this paper can be applied to various deep neural network architectures.
引文
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[ 3] Yang C,Liu P,Yin G,et al.Defect detection in magnetic tile images based on stationary wavelet transform[J].NDT & E International,2016,83:78-87
[ 4] Lee H,Kwon H.Going deeper with contextual CNN for hyperspectral image classification[J].IEEE Transactions on Image Processing,2017,26(10):4843-4855
[ 5] Graves A,Mohamed A R,Hinton G.Speech recognition with deep recurrent neural networks[C].In:IEEE International Conference on Acoustics ,Speech and Signal Processing,Vancouver,Canada,2013,6646-6649
[ 6] Saitoh T,Zhou Z H,Zhao G Y,et al.Concatenated frame image based CNN for visual speech recognition[C].In:Workshop on Multi-view Lip-reading Challenges,Asian Conference on Computer Vision,Taipei,China,2016.277-289
[ 7] He R,Wu X,Sun Z N,et al.Wasserstein cnn:Learning invariant features for nir-vis face recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2019,41(7):1761-1773
[ 8] Huang G B,Lee H,Learned-Miller E.Learning hierarchical representations for face verification with convolutional deep belief networks[C].In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,Providence,USA,2012.2518-2525
[ 9] 周飞燕,金林鹏,董军.卷积神经网络研究综述[J].计算机学报,2017,40(6):1229-1251
[10] Howard A G,Zhu M,Chen B,et al.Mobilenets:efficient convolutional neural networks for mobilevision applications[J].arXiv:1704.04861,2017
[11] Liu L Q,Shen C H,Henge A V D.The treasure beneath convolutional layers:cross-convolutional-layer pooling for image classification[C].In:IEEE Conference on Computer Vision and Pattern Recognition (CVPR),Boston,USA,2015.4749-4757
[12] Szegedy C,Liu W,Sermanet P,et al.Going deeper with convolutions[C].In:IEEE Conference on Computer Vision and Pattern Recognition(CVPR),Boston,USA,2015.1-9
[13] Wang J D,Zhen W,Zhang T,et al.Deeply-fused nets[J].arXiv:1605.07716,2016
[14] Giusti A,Ciresan D C,Masci J,et al.Fast image scanning with deep max—pooling convolutional neural networks[C].In:Proceedings of IEEE International Conference on Image Processing,Melbourne,Australia,2013.4034-4038
[15] Gong Y C,Wang L W,Guo R Q,et al.Multi-scale orderless pooling of deep convolutional activation features[C].In:Proceedings of the 13th European Conference on Computer Vision,Zurish,Switzerland,2014.392-407
[16] He K M,Zhang X Y,Ren S Q,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015 ,37 (9) :1904-1916
[17] Hu Q C,Wang H B,Li T,et al.Deep cnns with spatially weighted pooling for fine-grained car recognition[J].IEEE Transactions on Intelligent Transportation Systems,2017,18(11) :3147-3156
[18] Lee C Y,Gallagher P,Tu Z W.Generalizing pooling functions in CNNs:mixed,gated,and tree[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(4) :863-875
[19] Chan T H,Jia K,Gao S H,et al.PCANet:a simple deep learning baseline for image classification?[J].IEEE Transactions on Image Processing,2015,24(12):5017-5032
[20] Phan H,Koch P,Hertel L,et al.CNN-LTE:a class of 1-X pooling convolutional neural networks on label tree embeddings for audio scene classification[C].In:IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP),New Orleans,USA,2017.
[21] Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C].In:Proceedings of the 25th International Conference on Neural Information Processing Systems,Lake Tahoe,USA,2012.1097-1105
[22] He K M,Zhang X Y,Ren S Q,et al.Deep residual learning for image recognition[C].In:IEEE Conference on Computer Vision and Pattern Recognition,Las Vegas,USA,2016.770-778