Paper Defects Classification Based on VGG16 and Transfer Learning

There are some problems in traditional paper defects classification, such as the poor generalization performance, less types of recognition, and insufficient recognition accuracy. The deep learning method provides a new scheme for paper defects classification. However, due to the small sample size of paper defect images set, the over fitting phenomenon is easy to appear in the training process. Aiming this problem, a transfer learning method based on convolutional neural network model is proposed．Firstly, freezing the first seven construction layers of VGG16 network which has been trained by ImageNet, and fine tune the rest convolution layers with the paper defect images set to complete the feature extraction; Secondly, the full connection layers for classification are improved to meet the needs of paper defects classification; Finally, transfer learning strategy is adopted in the training process to improve the efficiency. The experimental results demonstrate that the paper defects classification proposed in our approach can improve the efficiency and accuracy of paper defects recognition. The approach will beneficial for the web inspection process.