2024
DOI: 10.1007/s11633-023-1459-z
|View full text |Cite
|
Sign up to set email alerts
|

Deep Industrial Image Anomaly Detection: A Survey

Jiaqi Liu,
Guoyang Xie,
Jinbao Wang
et al.

Abstract: The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the promising setting from industrial manufacturing and review the current IAD approaches under our proposed setting. Moreover, we highlight several openin… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
5
2

Relationship

0
7

Authors

Journals

citations
Cited by 55 publications
(2 citation statements)
references
References 157 publications
0
2
0
Order By: Relevance
“…Object detection and classification focus on identifying and locating specific objects in an image or data, using machine-learning techniques to assign appropriate labels or categories [39,40]. On the other hand, anomaly detection focuses on identifying abnormal or atypical patterns that deviate from the norm without specific object classes, using machine-learning techniques to detect deviations from expected behaviors [41][42][43]. Both approaches have different goals and strategies but utilize similar machine-learning tools.…”
Section: Classification Of Inclusions Vs Anomaly Detectionmentioning
confidence: 99%
“…Object detection and classification focus on identifying and locating specific objects in an image or data, using machine-learning techniques to assign appropriate labels or categories [39,40]. On the other hand, anomaly detection focuses on identifying abnormal or atypical patterns that deviate from the norm without specific object classes, using machine-learning techniques to detect deviations from expected behaviors [41][42][43]. Both approaches have different goals and strategies but utilize similar machine-learning tools.…”
Section: Classification Of Inclusions Vs Anomaly Detectionmentioning
confidence: 99%
“…Based on differences in features, traditional machine vision-based defect detection methods can be mainly classified into three categories: texture-based, color-based, and shape-based methods [15,16]. Song et al [17] successfully identified wood-grain defects using histogram features, achieving a recognition rate of 99.8%.…”
Section: Related Workmentioning
confidence: 99%