{"title":"DBAD: Dual branch reconstruction for industrial anomaly detection","authors":"Huaze Cai, Shuaishi Liu","doi":"10.1049/ell2.13289","DOIUrl":null,"url":null,"abstract":"<p>Reconstruction-based methods are commonly used in industrial visual anomaly detection. They rely on a well-reconstructed normal mode of the model. However, it is difficult to manage the boundary of generalization. The strength of the model's generalization capability can directly affect the fidelity of the reconstruction, resulting in the occurrence of false positives. To address the above challenges, a novel dual branch reconstruction anomaly detection approach is proposed to control the model generalization capability at two dimensions. It reconstructs abnormal images into normal ones by resolution recovery and denoising branches. Detection results are generated from their comparison. In addition, an innovative channel adjustment module is introduced to improve information exchange between branches. It uses multiple dilated convolutions for interactions over different scales. Simulation experiments demonstrate that the method outperforms most inspection methods on the MVTec AD and MVTec 3D-AD datasets. It also shows good results on the self-generated automotive paint scratches dataset of this study.</p>","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.13289","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics Letters","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ell2.13289","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Reconstruction-based methods are commonly used in industrial visual anomaly detection. They rely on a well-reconstructed normal mode of the model. However, it is difficult to manage the boundary of generalization. The strength of the model's generalization capability can directly affect the fidelity of the reconstruction, resulting in the occurrence of false positives. To address the above challenges, a novel dual branch reconstruction anomaly detection approach is proposed to control the model generalization capability at two dimensions. It reconstructs abnormal images into normal ones by resolution recovery and denoising branches. Detection results are generated from their comparison. In addition, an innovative channel adjustment module is introduced to improve information exchange between branches. It uses multiple dilated convolutions for interactions over different scales. Simulation experiments demonstrate that the method outperforms most inspection methods on the MVTec AD and MVTec 3D-AD datasets. It also shows good results on the self-generated automotive paint scratches dataset of this study.
期刊介绍:
Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews.
Scope
As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below.
Antennas and Propagation
Biomedical and Bioinspired Technologies, Signal Processing and Applications
Control Engineering
Electromagnetism: Theory, Materials and Devices
Electronic Circuits and Systems
Image, Video and Vision Processing and Applications
Information, Computing and Communications
Instrumentation and Measurement
Microwave Technology
Optical Communications
Photonics and Opto-Electronics
Power Electronics, Energy and Sustainability
Radar, Sonar and Navigation
Semiconductor Technology
Signal Processing
MIMO