Transformer-Based Detection of Microorganisms on High-Resolution Petri Dish Images
Abstract
Many medical or pharmaceutical processes have strict guidelines regarding continuous hygiene monitoring. This often involves the labor-intensive task of manually counting microorganisms in Petri dishes by trained personnel. Automation attempts often struggle due to major challenges: significant scaling differences, low separation, low contrast, etc. To address these challenges, we introduce AttnPAFPN, a high-resolution detection pipeline that leverages a novel transformer variation, the efficient-global self-attention mechanism. Our streamlined approach can be easily integrated in almost any multi-scale object detection pipeline. In a comprehensive evaluation on the publicly available AGAR dataset, we demonstrate the superior accuracy of our network over the current state-of-the-art. In order to demonstrate the task-independent performance of our approach, we perform further experiments on COCO and LIVECell datasets.
Cite
Text
Ebert et al. "Transformer-Based Detection of Microorganisms on High-Resolution Petri Dish Images." IEEE/CVF International Conference on Computer Vision Workshops, 2023. doi:10.1109/ICCVW60793.2023.00428Markdown
[Ebert et al. "Transformer-Based Detection of Microorganisms on High-Resolution Petri Dish Images." IEEE/CVF International Conference on Computer Vision Workshops, 2023.](https://mlanthology.org/iccvw/2023/ebert2023iccvw-transformerbased/) doi:10.1109/ICCVW60793.2023.00428BibTeX
@inproceedings{ebert2023iccvw-transformerbased,
title = {{Transformer-Based Detection of Microorganisms on High-Resolution Petri Dish Images}},
author = {Ebert, Nikolas and Stricker, Didier and Wasenmüller, Oliver},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2023},
pages = {3963-3972},
doi = {10.1109/ICCVW60793.2023.00428},
url = {https://mlanthology.org/iccvw/2023/ebert2023iccvw-transformerbased/}
}