Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks.
- DOI
- 10.1038/s41598-024-55864-2
- Published
- 2024 Mar 8
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-024-55864-2,
title = {Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks.},
author = {Siami M and Barszcz T and Wodecki J and Zimroz R},
year = {2024},
journal = {Scientific reports},
doi = {10.1038/s41598-024-55864-2},
url = {https://doi.org/10.1038/s41598-024-55864-2}
}RIS
TY - JOUR TI - Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks. AU - Siami M AU - Barszcz T AU - Wodecki J AU - Zimroz R PY - 2024 JO - Scientific reports DO - 10.1038/s41598-024-55864-2 UR - https://doi.org/10.1038/s41598-024-55864-2 ER -
APA
M, S., T, B., J, W., & R, Z. (2024). Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks.. Scientific reports. https://doi.org/10.1038/s41598-024-55864-2
Source records
- pubmed · retrieved 2026-11-10T12:15:52.832Z