Deep neural networks approach to microbial colony detection -- a comparative analysis

Abstract

Counting microbial colonies is a fundamental task in microbiology and has many applications in numerous industry branches. Despite this, current studies towards automatic microbial counting using artificial intelligence are hardly comparable due to the lack of unified methodology and the availability of large datasets. The recently introduced AGAR dataset is the answer to the second need, but the research carried out is still not exhaustive. To tackle this problem, we compared the performance of three well-known deep learning approaches for object detection on the AGAR dataset, namely two-stage, one-stage and transformer based neural networks. The achieved results may serve as a benchmark for future experiments.

8 pages, 2 figures, 3 tables

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• YOLOv4: Optimal Speed and Accuracy of Object Detection
• Transformers in Vision: A Survey
• Deformable DETR: Deformable Transformers for End-to-End Object Detection
• MMDetection: Open MMLab Detection Toolbox and Benchmark
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Cited by in corpus (1)

• Self-Normalized Density Map (SNDM) for Counting Microbiological Objects