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Pixel-Wise Defect Detection by CNNs without Manually Labeled Training Data.
- Source :
-
Applied Artificial Intelligence . 2019, Vol. 33 Issue 6, p548-566. 19p. - Publication Year :
- 2019
-
Abstract
- In machine learning driven surface inspection one often faces the issue that defects to be detected are difficult to make available for training, especially when pixel-wise labeling is required. Therefore, supervised approaches are not feasible in many cases. In this paper, this issue is circumvented by injecting synthetized defects into fault-free surface images. In this way, a fully convolutional neural network was trained for pixel-accurate defect detection on decorated plastic parts, reaching a pixel-wise PRC score of 78% compared to 8% that was reached by a state-of-the-art unsupervised anomaly detection method. In addition, it is demonstrated that a similarly good performance can be reached even when the network is trained on only five fault-free parts. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08839514
- Volume :
- 33
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Applied Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 135567089
- Full Text :
- https://doi.org/10.1080/08839514.2019.1583862