1. Pixel-Wise Defect Detection by CNNs without Manually Labeled Training Data.
- Author
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Haselmann, M. and Gruber, D. P.
- Subjects
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PIXELS , *CONVOLUTIONAL neural networks , *ANOMALY detection (Computer security) , *SURFACE defects , *ARTIFICIAL neural networks , *MACHINE learning - 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]
- Published
- 2019
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