用于无监督图像异常检测的局部-全局正态性学习和差异归一化流程

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
{"title":"用于无监督图像异常检测的局部-全局正态性学习和差异归一化流程","authors":"","doi":"10.1016/j.engappai.2024.109235","DOIUrl":null,"url":null,"abstract":"<div><p>The unsupervised detection and localization of image anomalies hold significant importance across various domains, particularly in industrial quality inspection. Despite its widespread utilization, this task remains inherently challenging due to its reliance solely on defect-free normal knowledge. This paper presents the local–global normality learning and discrepancy normalizing flow, a new state-of-the-art model for unsupervised image anomaly detection and localization. In contrast to existing methods, It adopts a two-stream approach that considers both local and global semantics, ensuring stable detection of abnormalities. The framework comprises two key components: the dual-branch Transformer and the discrepancy normalizing flow, facilitating reconstruction and discrimination. The proposed framework leverages pre-trained convolutional neural networks to extract multi-scale feature embeddings, followed by a novel dual-branch transformer that achieves feature reconstruction from local and global perspectives. The local reconstruction employs self-attention, while the global reconstruction incorporates global prototype tokens and semantic query tokens by the aggregation-cross attention mechanism. Moreover, discrepancy normalizing flow is developed to estimate the likelihood of anomalies based on the discrepancy between pre-trained features and local/global reconstruction results. Extensive validation on established public benchmarks confirms that our method achieves state-of-the-art performance with the proposed local–global reconstruction and discrimination dual-stream framework.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection\",\"authors\":\"\",\"doi\":\"10.1016/j.engappai.2024.109235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The unsupervised detection and localization of image anomalies hold significant importance across various domains, particularly in industrial quality inspection. Despite its widespread utilization, this task remains inherently challenging due to its reliance solely on defect-free normal knowledge. This paper presents the local–global normality learning and discrepancy normalizing flow, a new state-of-the-art model for unsupervised image anomaly detection and localization. In contrast to existing methods, It adopts a two-stream approach that considers both local and global semantics, ensuring stable detection of abnormalities. The framework comprises two key components: the dual-branch Transformer and the discrepancy normalizing flow, facilitating reconstruction and discrimination. The proposed framework leverages pre-trained convolutional neural networks to extract multi-scale feature embeddings, followed by a novel dual-branch transformer that achieves feature reconstruction from local and global perspectives. The local reconstruction employs self-attention, while the global reconstruction incorporates global prototype tokens and semantic query tokens by the aggregation-cross attention mechanism. Moreover, discrepancy normalizing flow is developed to estimate the likelihood of anomalies based on the discrepancy between pre-trained features and local/global reconstruction results. Extensive validation on established public benchmarks confirms that our method achieves state-of-the-art performance with the proposed local–global reconstruction and discrimination dual-stream framework.</p></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197624013939\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624013939","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

图像异常的无监督检测和定位在各个领域都具有重要意义,尤其是在工业质量检测领域。尽管这项任务得到了广泛应用,但由于其完全依赖于无缺陷的正常知识,因此在本质上仍具有挑战性。本文介绍了局部-全局常态学习和差异归一化流程,这是一种用于无监督图像异常检测和定位的最新模型。与现有方法相比,它采用双流方法,同时考虑局部和全局语义,确保稳定地检测异常。该框架由两个关键部分组成:双分支变换器和差异归一化流,有助于重建和判别。所提出的框架利用预先训练好的卷积神经网络提取多尺度特征嵌入,然后利用新颖的双分支变换器从局部和全局角度实现特征重构。局部重构采用自我关注,而全局重构则通过聚合-交叉关注机制纳入全局原型标记和语义查询标记。此外,还开发了差异归一化流程,根据预训练特征与局部/全局重构结果之间的差异来估计异常的可能性。在已建立的公共基准上进行的广泛验证证实,我们的方法与所提出的局部-全局重构和判别双流框架一起实现了最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection

The unsupervised detection and localization of image anomalies hold significant importance across various domains, particularly in industrial quality inspection. Despite its widespread utilization, this task remains inherently challenging due to its reliance solely on defect-free normal knowledge. This paper presents the local–global normality learning and discrepancy normalizing flow, a new state-of-the-art model for unsupervised image anomaly detection and localization. In contrast to existing methods, It adopts a two-stream approach that considers both local and global semantics, ensuring stable detection of abnormalities. The framework comprises two key components: the dual-branch Transformer and the discrepancy normalizing flow, facilitating reconstruction and discrimination. The proposed framework leverages pre-trained convolutional neural networks to extract multi-scale feature embeddings, followed by a novel dual-branch transformer that achieves feature reconstruction from local and global perspectives. The local reconstruction employs self-attention, while the global reconstruction incorporates global prototype tokens and semantic query tokens by the aggregation-cross attention mechanism. Moreover, discrepancy normalizing flow is developed to estimate the likelihood of anomalies based on the discrepancy between pre-trained features and local/global reconstruction results. Extensive validation on established public benchmarks confirms that our method achieves state-of-the-art performance with the proposed local–global reconstruction and discrimination dual-stream framework.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信