基于深度学习知识蒸馏的织物起球等级客观评定方法OA

With the continuous upgrading of quality demands in the global textile consumer market,fabric pilling has long been recognized as one of the core indicators that determine the quality and appearance durability of textile products and garments.Accordingly,the accuracy,efficiency and objectivity of fabric pilling grade evaluation have become a critical focus in the textile manufacturing and quality inspection industry.At present,the mainstream evaluation method for fabric pilling grade in industrial production still relies on manual visual inspection by professional inspectors under standardized lighting and environmental conditions.However,this manual evaluation method has inherent limitations:it is highly subjective,as the evaluation results are easily affected by the inspectors'professional experience,visual fatigue and emotional state,leading to frequent misjudgment of pilling grades.In addition,manual inspection suffers from extremely low efficiency,high labor cost,and difficulty in meeting the requirements of high-speed,continuous and full-inspection production in modern textile industry.With the rapid development of computer vision and artificial intelligence technology,the use of intelligent objective evaluation methods to replace manual inspection has become an inevitable development trend in the field of textile quality inspection. Aiming at the above problems,this paper proposed an objective evaluation method for fabric pilling grade based on deep learning knowledge distillation,which takes into account both evaluation accuracy and model lightweight.First,by analyzing the theory of deep learning knowledge distillation,a knowledge distillation-based fabric pilling grade evaluation model was constructed.Second,the ResNet-34 teacher model in the knowledge distillation grade evaluation model was improved to enhance its ability to learn and extract features of small pilling targets.Finally,the recommended method was used for experimental verification.This paper introduces deep learning knowledge distillation technology into the field of fabric pilling grade evaluation and improves the ResNet-34 teacher model in the knowledge distillation grade evaluation model,achieving a lightweight and highly accurate fabric pilling evaluation model.The experimental results show that the average accuracy of the proposed fabric pilling grade evaluation method reached 93.2%,an increase of 3.1 percentage points compared with the original ResNet-34 model.At the same time,the computational cost of the model was significantly reduced,enabling effective objective evaluation of fabric pilling grades. This study not only provides a new technical path for the objective and intelligent detection of fabric pilling,but also expands the application scope of deep learning knowledge distillation technology in the field of textile quality inspection.It also has important theoretical significance and practical application value for promoting the digital and intelligent transformation of the quality inspection link in the modern textile industry.