.png){kind=icon}
返回
A New Graph-Based Method for Class Imbalance in Surface Defect Recognition
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2021.3063755.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Surface defect recognition is an important technology to guarantee the quality of products in modern manufacturing systems, but the class imbalance in real-world production greatly influences its performance. Recently, some deep learning-based methods have been developed to address the class imbalance. Because of the interclass similarities and the intraclass variations (ISIV) of surface defect, it is hard to apply them to solve the coupling between class imbalance and ISIV. Graph-based methods, including graph convolutional network (GCN), have the potential for ISIV. Therefore, this article proposes a new graph-based method, named anchor-based class-balanced GCN (ACB-GCN), to solve the class imbalance in surface defect recognition. First, the proposed method constructs a class-balanced graph to address the problem that excessive information from majority classes influence the performance of graph convolution. Second, the proposed method defines anchor vectors in each defect to reduce the influence of ISIV on graph construction. The experimental results on four famous data sets with class imbalance demonstrate that the proposed method can effectively address the coupling between class imbalance and ISIV, and thus extract the discriminative features. Meanwhile, the proposed method achieves better performance than traditional methods and the original GCN, especially on minority classes.
Keyword:
Class imbalance
deep learning
graph-based methods
surface defect recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
暂无机构信息
引用论文
分享收藏
A systematic study of the class imbalance problem in convolutional neural networks卷积神经网络中类不平衡问题的系统研究.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization用于机械不平衡故障分类的深度归一化卷积神经网络及其可视化理解
PREAI
分享收藏Cost-sensitive ensemble of stacked denoising autoencoders for class imbalance problems in business domain用于业务领域中类不平衡问题的堆叠去噪自编码器的成本敏感集成
PREAI
分享收藏
查看更多内容