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Many medical or pharmaceutical processes have strict guidelines regardingcontinuous hygiene monitoring. This often involves the labor-intensive task ofmanually counting microorganisms in Petri dishes by trained personnel.Automation attempts often struggle due to major challenges: significant scalingdifferences, low separation, low contrast, etc. To address these challenges, weintroduce AttnPAFPN, a high-resolution detection pipeline that leverages anovel transformer variation, the efficient-global self-attention mechanism. Ourstreamlined approach can be easily integrated in almost any multi-scale objectdetection pipeline. In a comprehensive evaluation on the publicly availableAGAR dataset, we demonstrate the superior accuracy of our network over thecurrent state-of-the-art. In order to demonstrate the task-independentperformance of our approach, we perform further experiments on COCO andLIVECell datasets.

Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images