Name
Title | ||
|---|---|---|
Deep Learning-Based Industry Product Defect Detection with Low False Negative Error Tolerance |
Abstract | ||
|---|---|---|
Many methods for product defect detection have been proposed in the literature. The methods can be roughly divided into two categories, namely conventional statistical methods and machine learning-based ones. Especially for image-based defect detection, deep learning is known as the state-of-the-art. For product defect detection, the main issue is to reduce the false negative error rate (FNER) to almost zero, while keeping a relatively low false positive error rate (FPER). We can reduce the errors by introducing a rejection mechanism, but this approach may reject too many products for manual re-checking. In this study, we found that extremely low FNER can be achieved if we combine several techniques in using deep learning. In this paper, we introduce the techniques briefly, and provide experimental results to show how these techniques affect the performance for defect detection. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/iCAST51195.2020.9319407 | 2020 11th International Conference on Awareness Science and Technology (iCAST) |
Keywords | DocType | ISSN |
abnormal detection,deep learning,convolutional neural network,image classification | Conference | 2325-5986 |
ISBN | Citations | PageRank |
978-1-7281-9120-1 | 1 | 0.43 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsukasa Ueno | 1 | 1 | 0.77 |
| Qiangfu Zhao | 2 | 214 | 62.36 |
| Shota Nakada | 3 | 1 | 0.43 |