A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation
Abstract
:1. Introduction
- Inspired by the real-life industrial dataset that we gathered, we study the detection of 16 different types of steel surface defects, of which some are almost visually similar but have a different root cause. Moreover, for each detected surface defect, a severity grading is also to be produced by the inspection system in order to produce a fine-grained quality report. We demonstrate that the joint training of these two tasks simultaneously yields better results than when treating them separately.
- We strive towards pixel-perfect segmentation of the surface defects. We show that our network can produce these, although some of the annotations of our dataset are in the less precise but cheaper bounding boxes format instead of pixel-perfect segmentation maps.
- We demonstrate that fusing the image data with steel production process parameters during classification is very advantageous for the detection accuracy of the proposed system.
2. Method
2.1. Dataset
2.2. Semantic Segmentation Using U-Net
Multi-Branch U-Net
2.3. Loss Function
3. Experiments and Results
3.1. Single Task Networks
3.2. Multi-Branch Network Combing Defect Type and Severity
Multi-Branch U-Net
3.3. Using Process Parameters as Extra Supervision
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Defect Type | Severity | |||||||
---|---|---|---|---|---|---|---|---|
Model | mIoU | mACC | fwIoU | pACC | mIoU | mACC | fwIoU | pACC |
Single-Task | 31.9 | 49.8 | 83.2 | 88.6 | 37.8 | 55.3 | 81.2 | 85.6 |
Multi-Branch | 33.4 | 51.7 | 84.1 | 89.2 | 40.8 | 59.5 | 82.3 | 86.1 |
Multi-Branch with Process Params | 40.2 | 73.9 | 85.4 | 90.1 | 43.7 | 66.4 | 83.4 | 88.2 |
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Neven, R.; Goedemé, T. A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation. Metals 2021, 11, 870. https://doi.org/10.3390/met11060870
Neven R, Goedemé T. A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation. Metals. 2021; 11(6):870. https://doi.org/10.3390/met11060870
Chicago/Turabian StyleNeven, Robby, and Toon Goedemé. 2021. "A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation" Metals 11, no. 6: 870. https://doi.org/10.3390/met11060870