Knitted fabric and nonwoven fabric pilling objective evaluation based on SONet

Anti-pilling performance is an important indicator of fabric. In order to overcome the inefficiency and poor consistency of subjective pilling rating in the industry, we propose an objective rating method based on a convolutional neural network (CNN). We begin by establishing a pilling image dataset of four different fabrics and three different hairball shapes, including knitted fabric and nonwoven fabric. Next, we use a SONet rating system model. The model consists of two branches, S branch and O branch. The S branch extracts the hairball feature of the pilling image through an attention mechanism, while the O branch extracts the hairball feature and fabric texture feature by factorizing the mixed feature maps according to frequency. The results show that the rating accuracy of the proposed SONet model reaches 97.70%. Finally, we demonstrate the reliability of the SONet model in the objective evaluation of fabric pilling using a feature map, heat map and class probability scatter plot.

Automated summary

The study proposed an objective pilling rating method based on CNN, established a pilling image dataset of various fabrics and hairball shapes, and achieved a rating accuracy of 97.70% with the SONet rating system model.