Abstract
In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyper-parameters tuning experiments. During the experiments, we find complementarities among the 21 models, which is the basis for feature fusion related experiments. We also find that the data augmentation method of geometric deformation is difficult to improve the performance of VTs (ViT, DeiT, BotNet, and T2T-ViT) series models. In terms of model performance, Xception has the best classification performance, the vision transformer (ViT) model consumes the least time for training, and the ShuffleNet-V2 model has the least number of parameters.
Highlights
With the advancement of industrialization, industrial pollution becomes increasingly serious
The model is more effective than Convolutional Neural Networks (CNNs) on super-large-scale datasets and has high computational efficiency (Dosovitskiy et al, 2020)
Taking the Accuracy index as an example, the accuracy of the vision transformer (ViT) model in the test set has dropped by -2.5%, the Accuracy of the T2T-ViT model is equal to that before the augmentation, and the Accuracy of the Deit model has only increased by 1.16%
Summary
With the advancement of industrialization, industrial pollution becomes increasingly serious. Biological approaches have outstanding performance in solving environmental pollution problems. The Biological approaches have four main advantages in environmental treatment: no new pollution, no additional energy consumption, gentle process, decomposition products can feedback to nature, and make a virtuous cycle of material changes (McKinney, 2004). Microorganisms are all tiny creatures that are invisible to the naked eyes. They are tiny and simple in structure, and usually can only be seen with a microscope. Lactic acid bacteria can decompose some organic matter in the natural environment to provide nutrients for plants; actinomycetes can digest organic waste in sludge and improve water quality; microalgae can fix carbon dioxide in the air and be used as a raw material for biodiesel (Zhao et al, 2021); activated sludge composed of microorganisms has a strong ability to adsorb and oxidize organic matter and purify water (Asgharnejad and Sarrafzadeh, 2020). EMs research helps solve environmental pollution problems, and the classification of EMs is the cornerstone of related research (Kosov et al, 2018)
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