.png){kind=icon}
返回
Environmentally-Robust Defect Classification With Domain Augmentation Framework
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3453371.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Visual defect classification is a critical process in manufacturing systems, aiming to achieve high-quality production and reduce costs. Although deep learning-based defect classification models have achieved significant success, their performance can be significantly diminished due to 'environment shifts'-variations in manufacturing environments across multiple production lines. To address this challenge, we propose a domain augmentation framework to construct an environmentally-robust defect classification model, delivering high performance across various manufacturing environments using a training dataset from only a single production line. In our framework, each environment is treated as a separate domain, and multiple augmented domains are created using image transformation functions. Subsequently, a defect classification model is trained using a multi-source domain generalization (DG) method with these augmented domains. This approach mitigates the single-source DG problem to a multi-source DG problem, enabling the adoption of multi-source DG methods, which leads to performance improvements. The effectiveness of the proposed framework is demonstrated through experiments on a dataset provided by a Korean manufacturing company.
Keyword:
Training
Production
Data models
Training data
Costs
Manufacturing systems
Image augmentation
Visual analytics
Defect detection
Classification algorithms
Visual defect classification
environment shift
domain generalization
image augmentation
manufacturing system
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
暂无机构信息
引用论文
The International Association for the Study of Pain definition of pain: as valid in 2018 as in 1979, but in need of regularly updated footnotes.png)
PAIN Reports
OAAI
PAIN ReportsIF0
OAAI分享收藏Do Perceptions of Competence Mediate The Relationship Between Fundamental Motor Skill Proficiency and Physical Activity Levels of Children in Kindergarten?能力的感知是否可以介导幼儿园儿童的基本运动技能熟练程度与身体活动水平之间的关系？
PREAI
分享收藏Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and PerspectivesIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Synthetic data augmentation for surface defect detection and classification using deep learning
PREAI
分享收藏
查看更多内容