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

Abstract

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 function 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%.