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.

翻译：许多医疗或制药流程对持续卫生监测有严格规范，这通常涉及由经过培训的人员手动计数培养皿中微生物的劳动力密集型任务。自动化尝试常因三大挑战而受阻：显著的尺度差异、低分离度、低对比度等。为应对这些挑战，我们提出AttnPAFPN——一种利用新型Transformer变体（高效全局自注意力机制）的高分辨率检测管道。我们的精简方法可轻松集成至几乎任意多尺度目标检测管道中。在公开AGAR数据集上的全面评估表明，我们网络的准确率超越当前最先进水平。为证明方法的任务无关性，我们还在COCO和LIVECell数据集上进行了进一步实验。