Fabric Defect Detection Based on the Improved Cascade R-CNN

Abstract

There is a large amount of fabric produced in the process of industrial production, thus fabric defects automatic detection can bring great benefits to enterprises. With the development of computer technology, deep learning has more advantages on fabric defect detection compared with traditional image processing. By comparing the advantages and disadvantages of different target detection models, we chose to use the Cascade R-CNN model for fabric defect detection finally. However, due to the large size of fabric image, small number of partial defects and complex image background, there are many difficulties in fabric defect detection. To solve the difficulties, we propose some methods to improve the accuracy of defect detection. First, in order to enhance the detection accuracy of small targets, we cut large fabric images into blocks for training and detection, and combine the detection results. Then, to solve the problem of the small number of partial defects in the fabric Dataset, a new method of multi-morphological data augmentation was proposed to increase the number of the Dataset. Finally, we improved the Feature Pyramid Networks module to enhance positional accuracy of defect detection. Experimental results show that the model can complete the task of defect detection efficiently and improve the accuracy of target detection effectively.

Intelligent Quality Control of Surface Defects in Fabrics: A Comprehensive Research Progress

“…Detecting fabric defects in complex backgrounds and large-sized images remains a considerable challenge. In light of this, Xue et al [97] selected the Cascade R-CNN as the baseline model, divided large fabric images into smaller chunks for training and detection, proposed a novel polymorphic data expansion method to augment the dataset size, enhanced the feature pyramid network module, and introduced the PAFPN model to improve defect detection accuracy. The proposed method achieved a detection accuracy of 78.93% on high-resolution fabric images, effectively addressing the detection of oversized defects, tiny defects, small defects, and dense defects.…”