Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images
This work addresses automation challenges in hygiene monitoring for medical or pharmaceutical processes, though it appears incremental as it builds on existing multi-scale object detection pipelines.
The paper tackles the labor-intensive task of manually counting microorganisms on Petri dishes by introducing AttnPAFPN, a high-resolution detection pipeline using a novel transformer variation, which achieves superior accuracy over state-of-the-art methods on the AGAR dataset.
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.