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Abstract
Fabric fuzzing and pilling have long been a problem in the textile industry, and consumer complaints about fabric fuzzing and pilling account for about 29.5% of the total textile product quality problems every year. At present, more and more subjective rating methods are used at home and abroad to rate fabric pilling, but there are still certain limitations, such as slow speed and subjectivity, which prompted the exploration of an accurate and objective automatic rating method to become a very urgent need. In response to these problems, this work is based on the multi-feature fusion convolutional neural network (CNN) Light-Smart-Network (LSNet) that has the capability to objectively grade fabric pilling. The fuzzing and pilling data is first set for six kinds of knitted fabrics, woven fabrics, and non-woven fabrics; next, the LSNet network determines the best parameters for objective rating. The network model is composed of a SqueezeNet branch (S1 Branch) and a ShuffleNet branch (S2 Branch), where the S1 Branch uses deep separable convolution (DSC) to improve the Fire module of the SqueezeNet network and adds an attention mechanism that makes the network favor contextual information. An S2 Branch adds short connections to the ShuffleNet network to combine feature maps of different sizes, perform fusion, enhance the network’s ability to extract feature map information, and optimize some details. Experiments show that the accuracy of LSNet’s objective rating for fabric fuzzing and pilling is 97.386% with a model size of only 5.25 M. Finally, the LSNet model is verified via a heat and feature map, which proves that LSNet has an objective rating for fabric fuzzing and pilling reliability.
Disclosure statement
No potential conflict of interest was reported by the authors.