Defect detection method of magnetic disk image based on improved convolutional neural network

Ming-hai YAO; Meng-li MA; Jia-min LIU

doi:10.1007/s11801-020-9183-1

[1] L Xie, L Lin, M Yin, L Meng and G Yin, Applied Surface Science 118, 375 (2016).

[2] Li D, Liang LQ and Zhang WJ, Int. J. Adv. Manuf. Technol. 73, 1605 (2014).

[3] X Li, H Jiang and G Yin, NDT&E Int. 62, 6 (2014).

[4] Redmon J., Divvala S., Girshick R. and Farhadi A., You Only Look Once: Unified, Real-Time Object Detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779 (2016).

[5] Zou MaoYang and Zhong Yong, Sensing and Imaging 19, 8 (2018).

[6] Collobert R., Weston J., Bottou L., Karlen M., Kavukcuoglu K. and Kuksa P., Journal of Machine Learning Research 12, 2493 (2011).

[7] Naderi N. and Nasersharif B., Multiresolution Convolutional Neural Network for Robust Speech Recognition, IEEE Iranian Conference on Electrical Engineering, 1459 (2017).

[8] Lim W., Jang D. and Lee T., Speech Emotion Recognition Using Convolutional and Recurrent Neural Networks, IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016.

[9] Liu P, Zhang H and Zhang K, Multi-level Wavelet-CNN for Image Restoration, IEEE Conference on Computer Vision and Pattern Recognition, 773 (2018).

[10] Yin Shi-hao, Deng Ji-cai, Ma Yan, Du Jing-yuan and Shang Xiao-kai, Optoelectronics Letters 14, 447 (2018).

[11] Nair V. and Hinton G., Rectified Linear Units Improve Restricted Boltzmann Machines, In ICML, 2010.

[12] Ioffe S. and Szegedy C., Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv preprint arXiv:1502.03167, 2015.

[13] Klambauer G., Unterthiner T., Mayr A. and Hochreiter S., Self-normalizing Neural Networks, Advances in Neural Information Processing Systems, 2017.

[14] Simonyan K. and Zisserman A., Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint arXiv:1409.1556, 2015.

[15] Clevert DA., Unterthiner T. and Hochreiter S., Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs), arXiv preprint arXiv:1511.07289, 2015.

[16] Martín A., Paul B., Jianmin C., Zhifeng Chen, Andy D., Jeffrey D., Matthieu D., Sanjay G., Geoffrey I., Michael I., Manjunath K., Josh L., Rajat M., Sherry M., Derek G. M., Benoit S., Paul T., Vijay V., Pete W., Martin W., Yu Yuan and Zheng Xiaoqiang, Tensorflow: A System for Large-scale Machine Learning, Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

[17] Glorot X. and Bengio Y., Understanding the Difficulty of Training Deep Feedforward Neural Networks, In AISTATS, 2010.

[18] Kingma DP. and Ba J., Adam: A Method for Stochastic Optimization, arXiv preprint arXiv:1412.6980, 2014.

[19] Krizhevsky A, Sutskever I and Hinton G E, ImageNet Classification with Deep Convolutional Neural Networks, Proceedings of the 25th International Conference on Neural Information Processing Systems, 1097 (2012).

[20] He Kaiming, Zhang Xiangyu, Ren Shaoqing and Sun Jian, Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770 (2016).