Objective rating method for fabric pilling combining multiscale features and attention mechanism

With the continuous improvement of processes by textile manufacturers, the requirements for pilling of fabric materials are also increasing. At present, in the textile industry, the rating method for fabric pilling is to compare the sample with the standard sample in a specific environment to determine the grade of fabric pilling. However, this method is greatly influenced by subjective factors, and when facing a large number of samples, it consumes a large amount of manpower and material resources. Therefore, an objective, stable, and highly accurate rating method is needed. This paper proposes an end-to-end objective rating method based on a convolutional neural network model called DENet. The network employs a two-branch architecture that integrates an attention mechanism with multiscale convolution and connects large convolutional kernels across layers. To align with the subjective grading criteria for fabric pilling, this study simulates the pilling process of eight types of fabrics using two methods: the pilling box method and the circular trajectory pilling method. Consequently, eight fabric pilling image datasets were generated, covering knitted fabrics, woven fabrics, and nonwoven fabrics, including two patterned fabric types. The experiment results show that the accuracy of the rating method proposed in this paper is 97.95% for combined training on eight datasets, and the average accuracy is 98.07% for separate training on eight datasets, and the accuracy and generalization of the designed network model is high. The proposed system provides an efficient, objective, and stable solution for fabric pilling evaluation.