mlsanityData to 7 Oct 2026

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

arXiv 2203.09474 ↗First posted 15 Mar 2022cs.CV cs.AI cs.LGcited by 1

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Abstract (arXiv)

The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U2-Net. Two statistical methods for deep neural networks are utilized: the bootstrap and the Monte Carlo (MC) dropout. The detailed analysis of the uncertainties for the DM predictions leads to a deeper understanding of the DM model's deficiencies. Based on our investigation, we propose a self-normalization module in the network. The improved network model, called textitSelf-Normalized Density Map (SNDM), can correct its output density map by itself to accurately predict the total number of objects in the image. The SNDM architecture outperforms the original model. Moreover, both statistical frameworks -- bootstrap and MC dropout -- have consistent statistical results for SNDM, which were not observed in the original model. The SNDM efficiency is comparable with the detector-base models, such as Faster and Cascade R-CNN detectors.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 50 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

21 of 50 references.

References · 21

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9 more
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Citing papers per year: 2023: 1.

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  1. 2023Deep learning for diffusion in porous mediacited by 1