Paper Defects Recognition Based on Deformable Convolution

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, convolutional neural network has strict requirements for the size of the input image. This requires that in the process of practical engineering application, for the collected paper defect images to be classified, the area containing paper defect must be segmented during preprocessing, and then the size of the paper defect area must be adjusted to meet the input requirements of the adopted classifier. To solve the above problems, the two-stage target detection network Faster R-CNN (Region-Convolutional Neural Network) was used in paper defects recognition to solve the problem of the size requirements of the input image; In addition, the deformable convolution layer was added after the traditional convolution layer to learn the characteristics of paper defects more efficiently and accurately, so as to improve the accuracy and accuracy of paper defects recognition and classification; Finally, the deformable RoI (Region-of-Interest) pooling layer was used to replace the RoI pooling layer of classic Faster R-CNN to locate and classify the paper defects area more accurately. Experiments show that the proposed algorithm has a further improvement in accuracy and scalability compared with the previous algorithm.