计算机与现代化Issue(8)：69-76,8.DOI:10.3969/j.issn.1006-2475.2026.08.009
融合多分支特征与前景挖掘的异常检测方法
An Anomaly Detection Method Integrating Multi-branch Features with Foreground Mining
摘要
Abstract
In the automated anomaly detection of supermarket goods packaging,due to the complexity and diversity of packaging design,anomaly features are often concealed within complex background information,significantly increasing the difficulty of identification.Focus on the limitations of existing methods that overly rely on predefined universal feature extraction modules,which results in ineffective extraction of anomalous features,imprecise analysis of anomaly patterns,and inaccurate localization of abnormal regions in complex scenarios,this paper proposes an anomaly detection method integrating Multi-Branch Features with Foreground Mining(MBFFM).Firstly,the proposed method uses synthetic anomalies to enhance the student network's abil-ity to perceive anomalies,and promote it to build more accurate decision boundaries in the feature space.Secondly,a multi-branch feature extractor is introduced to extract and fuse multi-level and multi-scale features to obtain rich texture and semantic information,and enhance the adaptability of the model to the actual scene.Then,the foreground mining loss is used to reduce the error of foreground reconstruction,supplement local structural information,and improve the detection ability of the model to structural anomalies.The comprehensive performance of the proposed method on the GoodsAD dataset outperforms eight SOTA methods.The qualitative,quantitative and ablation experimental results verify the effectiveness of the internal modules within the overall framework of the proposed method.关键词
异常检测/无监督学习/知识蒸馏/蒸馏损失/前景挖掘Key words
anomaly detection/unsupervised learning/knowledge distillation/distillation loss/foreground mining分类
信息技术与安全科学引用本文复制引用
黄建一,贾志谦,滕升华,李佐勇..融合多分支特征与前景挖掘的异常检测方法[J].计算机与现代化,2026,(8):69-76,8.基金项目
国家自然科学基金面上项目(62471207) （62471207）
福建省自然科学基金资助项目(2024J02029,2024J08281) （2024J02029,2024J08281）
闽江学院引进人才科研项目(MJY23035) （MJY23035）
闽江学院校级专项课题(MJJD25001) （MJJD25001）
