.png){kind=icon}
返回
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization
.png){kind=icon}
OA
AI
DOI：10.1109/TII.2023.3241579.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate the overgeneralization, this article proposes to collaboratively optimize normal and abnormal feature distributions with the assistance of synthetic anomalies, namely collaborative discrepancy optimization (CDO). CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i.e., the margin and the overlap between the discrepancy distributions (DDs) of normal and abnormal samples. With CDO, a large margin and a small overlap between normal and abnormal DDs are obtained, and the prediction reliability is boosted. Experiments on MVTec2D and MVTec3D show that CDO effectively mitigates the overgeneralization and achieves great anomaly localization performance with real-time computation efficiency. A real-world automotive plastic parts inspection application further demonstrates the capability of the proposed CDO.
Keyword:
Anomaly localization
collaborative discrepancy
optimization (CDO)
computer vision
defect detection.
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏分享收藏First-Principles Computation of the Vibrational Entropy of Ordered and DisorderedNi3Al有序和无序振动熵的第一性原理计算 Ni3Al
PREAI
分享收藏The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly DetectionMVTec异常检测数据集: 用于无监督异常检测的全面真实数据集
OAAI
OAAI分享收藏
查看更多内容