.png){kind=icon}
返回
A learning-based approach for surface defect detection using small image datasets
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2019.09.107.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Quality management is a fundamental component of a manufacturing process. In this paper, we propose a promising learning-based approach for automatic defect detection based on small image datasets. With the help of Wasserstein generative adversarial nets (WGANs), feature-extraction-based transfer learning techniques, and multi-model ensemble framework, our approach is able to deal with imbalanced and severely rare images with defects successfully, which is practically useful to the manufacturing industry. In addition, we reduce the false negative rate (FNR) as much as possible. Extensive experiments of defect detection on decorative sheets and welding joints achieve FNR accuracy results as 0.47% and 1.9% respectively, while traditional vision methods using in the production line can only achieve FNR results at about 20% under the similar circumstance, thus substantiating the proposed approach is quite effective for surface defect detection. (c) 2020 Elsevier B.V. All rights reserved.
Keyword:
Defect detection
Convolutional neural network
Transfer learning
Multi-model ensemble
Generative adversarial nets(GANs)
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏
分享收藏分享收藏Selective toxicity of pyrethroid insecticides to arthropod natural enemies and pests of agricultural crops.png)
Entomophaga
EntomophagaIF0
PREAI
分享收藏 没有更多内容
没有更多内容