机械科学与技术2026，Vol.45Issue(2)：270-280,11.DOI:10.13433/j.cnki.1003-8728.20240001
面向纹理表面缺陷检测的增量学习方法
Incremental Learning Method of Defect Detection for Texture Surface
摘要
Abstract
In application,production lines continuously provide new training samples and defect types,requiring models to use learned knowledge and combine new samples to learn quickly and have incremental learning ability.To address this issue,an incremental learning method of defect detection for texture surface is proposed.The present algorithm consists of an adaptive convolution/transposed convolution module and a texture surface defect detection network.The former allocates weights to measure the relevance of the detection model parameters to the current training category and endows the model with incremental learning ability.The latter designs reconstruction and segmentation branches,and combines adversarial learning to improve the reconstruction quality and segmentation performance of the model for defects in texture surface.The present algorithm simulates the incremental learning on the MvTec AD texture class of defect detection public dataset,achieving an AUROC accuracy index of 97.6%.The effectiveness of the present module in ablation experiments is also verified.The research evaluates the performance of the present algorithm in real-world scenarios by collecting and testing data from printed substrates on the production line.The achieved average detection rate of 98.7%across four classes serves to validate the application of the algorithm.关键词
纹理表面缺陷检测/增量学习Key words
texture surface defect detection/incremental learning分类
信息技术与安全科学引用本文复制引用
何大伟,杨华..面向纹理表面缺陷检测的增量学习方法[J].机械科学与技术,2026,45(2):270-280,11.基金项目
国家自然科学基金联合基金重点项目(U22A20208)、湖北省自然科学基金创新研究群体项目(2022CFA018)及佛山市产业领域科技攻关专项(2020001006509) （U22A20208）
