光学精密工程2026，Vol.34Issue(2)：296-308,13.DOI:10.37188/OPE.20263402.0296
高频引导的多尺度空间感知异常检测网络
Multi-scale spatial sensing anomaly detection network guided by high frequency
摘要
Abstract
Aiming at the problems of low detection accuracy for small-sized defects,weak multi-scale fea-ture extraction ability and low anomaly segmentation accuracy of existing industrial anomaly detection algo-rithms,an industrial anomaly detection network combining high-frequency residual guidance and multi-scale attention feature fusion was proposed.Firstly,aiming at the problem of high-frequency detail loss caused by traditional full-frequency processing,a frequency-domain separation strategy was designed.Gaussian kernel filtering was utilized to extract high-frequency residual features,enhancing the network's detection ability for minor anomalies.Secondly,aiming at the problems of insufficient representation abili-ty of complex textures and low discrimination between anomalies and backgrounds in conventional convolu-tional networks,a globally enhanced multi-scale attention module GEMA is embedded in the encoder stage of the discriminative network.It captures multi-scale local information in the horizontal and vertical directions through parallel dual-path,enhancing the salient features at different spatial positions.Improve the feature discriminability in complex texture backgrounds;Finally,in the decoder stage of the discrimi-nant network,the coordinate attention module CoordAtt is integrated.By decomposing the coordinate ax-es and dynamically modulating the feature weights,precise spatial positioning of abnormal areas is achieved.Experiments show that on the MVTec AD public dataset,the average AUROC at the image level of the improved model is 98.6％,and the average AUROC and AP at the pixel level are 97.6％and 73.2％respectively,effectively improving the effect of industrial anomaly detection.关键词
工业异常检测/高频分量引导/多尺度空间感知/注意力机制Key words
industrial anomaly detection/high-frequency component guidance/multi-scale spatial percep-tion/attention mechanism分类
机械制造引用本文复制引用
张陈涛,邹庆林,刘洋,王亚飞,王彩云,徐周毅,郑高峰..高频引导的多尺度空间感知异常检测网络[J].光学精密工程,2026,34(2):296-308,13.基金项目
国家乳业技术创新中心项目(No.2024-JSGG-008) （No.2024-JSGG-008）
内蒙古自治区中央引导地方科技发展资金项目(No.2024ZY0074) （No.2024ZY0074）
