YG502N fabric pilling tester parameters.

The degree of pilling and fuzzing in textile fabrics is a crucial indicator of textile product quality. Current evaluation methods predominantly rely on subjective judgments, leading to issues such as rating errors and inefficiency. To achieve objective assessment of pilling and fuzzing grades, this study proposes a Hybrid Feature-Based Machine Vision Method for Objective Evaluation of Textile Pilling and Fuzzing. The method incorporates a Hybrid Feature-based Depthwise Separable Attention Network for Objective Evaluation of Textile Pilling and Fuzzing (HDAN-PF), which effectively extracts and fuses frequency and Space domain features. A Channel Attention mechanism enhances the model’s ability to capture subtle features, while Depthwise Separable Convolutions reduce computational complexity, improving evaluation speed while maintaining high accuracy.The model size is approximately 327.37 MB with a total parameter count of 135,115,512. Experimental results demonstrate that the proposed method achieves a classification accuracy of 96.26% on diverse fabric images, showcasing robust generalization and practical utility.By leveraging this machine vision approach, the proposed method offers a transformative solution for achieving objective, consistent, and efficient assessments of pilling and fuzzing grades, advancing textile quality evaluation practices.

纺织面料的起球与起毛程度是纺织产品质量的核心评判指标。当前主流的评估方法多依赖主观判断，易产生评级误差且评估效率低下。为实现起球与起毛等级的客观评估，本研究提出一种基于混合特征的机器视觉方法，用于纺织面料起球起毛的客观评级。该方法引入了面向纺织起球起毛客观评级的混合特征深度可分离注意力网络（HDAN-PF），可有效提取并融合频域与空域特征。通道注意力机制可提升模型捕捉细微特征的能力，而深度可分离卷积则能降低计算复杂度，在保持高精度的同时提升评估速度。该模型大小约为327.37 MB，总参数量达135,115,512。实验结果表明，所提方法在多样化的面料图像数据集上的分类准确率可达96.26%，展现出优异的鲁棒泛化能力与实用价值。借助该机器视觉技术路径，本方法为实现起球起毛等级的客观、一致且高效的评估提供了突破性解决方案，推动了纺织质量评估行业的发展。