纺织高校基础科学学报2025，Vol.38Issue(3)：48-57,10.DOI:10.13338/j.issn.1006-8341.2025.03.006
基于FSFF-UCDAE的无监督织物疵点检测
Unsupervised fabric defect detection based on FSFF-UCDAE
摘要
Abstract
Supervised models heavily rely on scarce defect samples and require extensive manual anno-tation,making it challenging to meet practical application needs.To address this issue,the research team proposed an unsupervised fabric defect detection method based on image reconstruction and residual processing.During the training phase,only defect-free samples were used as the training set.By constructing and training a reconstruction model,the method learns and represents the normal structural characteristics of fabrics.In the detection phase,the model reconstructs the ima-ges to be inspected and calculates the residuals between the original and reconstructed images.Thresholding and morphological processing was then applied to accurately extract and locate fab-ric defect regions.Experimental results demonstrate that the method not only effectively avoids reliance on defect samples but also accurately detects fabric defect regions.Its high accuracy and robustness are further ralidate by comparing with other models.关键词
织物疵点检测/去噪自编码器/无监督学习/全尺度特征融合/图像重构Key words
fabric defect detection/autoencoder/unsupervised learning/full-scale feature fusion/image reconstruction分类
轻工纺织引用本文复制引用
张周强,王康旭,李成,张金旭,王林..基于FSFF-UCDAE的无监督织物疵点检测[J].纺织高校基础科学学报,2025,38(3):48-57,10.基金项目
功能性纺织材料及制品教育部重点实验室资助项目(2024FTMP016) （2024FTMP016）
陕西省自然科学基础研究计划项目(2023JCYB288) （2023JCYB288）
西安工程大学专业学位研究生教学案例项目(24yjxa109) （24yjxa109）
