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

• 作者: LIU Jia-min MA Meng-li YAO Ming-hai
• 来源期刊: 光电子快报（英文版）
• 年，卷（期）: 2020,（5）
• 所属期刊栏目: Image and Information Processing
• 页码范围: 396-400
• 页数: 5页
• 语种: 英文
• DOI: 10.1007/s11801-020-9183-1

摘要

This paper proposes an efficient method for defect detection of magnetic disk image based on improved convolutional neural network.We build a model named DiskNet on the basis of VGGNet-19,in which the optimal activation func tion is selected predictively through a weighted probability learning curve model (WP-Model).First,we use Markov Chain Monte Carlo (MCMC) to infer the predicted value and determine prediction probability.Then,the evaluation point (EP) is determined by the effective information of training curve.In the process of DiskNet training,when the prediction probability is higher than the threshold,the neural network will select the current activation function.If the training epochs exceed the EP and the threshold is not reached,the original activation function will be used.The experimental results show that the accuracy of the proposed method in detecting defects on the magnetic disk image data set is 96.9％.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	YAO Ming-hai		1	0	0.0	0.0
2	MA Meng-li		1	0	0.0	0.0
3	LIU Jia-min		1	0	0.0	0.0