## 1 Introduction  
With the development of industrialization, environmental pollution has become a vital problem to be resolved urgently. Compared with the classical physical and chemical approaches, the novel biological methods are more efficient and transparent, causing no secondary pollution, which has become the preference for environmental pollution. The research of _Environmental Microorganisms_ (EMs) is helpful to focus on the interrelationship among microorganisms, pollutants and the environment. It is essential to use microorganisms to degrade the increasingly severe and diverse environmental pollutants effectively. Yeast is a kind of single-celled eukaryotic microorganism that is highly adaptable to the environment. It is widely applied to produce alcohol, glycerol and organic acids, which are closely linked to the life and production activity of humanity. Until now, yeast have also been used in the treatment of toxic industrial wastewater and solid waste, which plays an important role in treating environmental pollution [1; 2]. In the research of yeast applied in industrial production and environmental pollution control, biomass is the basic evaluation method and can quantitatively consider the performance of yeast in various tasks [3]. At present, there are mainly two types of counting methods. The first is manual counting methods, such as plate counting and hemocytometry; another is semi-automatic counting methods, such as flow cytometry [4; 5]. Manual counting is straightforward and stable to use with high accuracy when the number of cells is limited. However, when the number of cells becomes larger, it will be time-consuming, and the accuracy will be lowered due to the subjective influence of the operator. Semi-automatic counting is more accurate and can obtain ideal results in the case of large biomass; however, it is not portable and requires expensive equipment [6]. Therefore, these classical methods have non-negligible limitations in practice. Due to the rapid developments of computer vision and deep learning technologies, computer-assisted image analysis is broadly applied in many research fields, including histopathological image analysis [7; 8; 9; 10], cytopathological image analysis [11; 12; 13], object detection [14; 15; 16], microorganism classification [17; 18; 19; 20; 21; 22], microorganism segmentation [23; 24; 25; 26] and microorganism counting [27; 28]. However, by reviewing the works of microorganism counting from the 1980s until now [28], we find that, in the process of image segmentation, all existing segmentation approaches use traditional technologies, such as thresholding [29], edge detection [30] and watershed [31]. Most of the deep-learning approaches are only applied for microorganism classification but not for microorganism segmentation in the task of microorganism counting [32]. Here, we propose a novel _Pixel Interval Down-sampling Network_ (PID-Net) for the yeast counting task with higher accuracy. The PID-Net is an improved Convolutional Neural Network (CNN) based on an encoder-decoder architecture, pixel interval down-sampling and concatenate operations. By comparing with the traditional SegNet [33] and U-Net [34]-based object counting algorithms, the accuracy of counting is improved. The workflow of the proposed PID-Net counting method is shown in Figure 1.  
In Figure 1, (a) Original Dataset: The dataset contains images of yeast cells and their ground truth (GT). The range is from 1 to 256 yeast cells in each image. (b) Data Augmentation: Mirror and rotation operations are applied to augment the original dataset. (c) Training Process: PID-Net is trained for image segmentation and the best model is generated. (d) Segmentation Result: Test images are processed using the trained PID-Net model and output the predicted segmentation results. (e) Counting Result: The number of yeast cells is counted by using connected domain detection. The main contributions of this paper are as follows:  
* We propose PID-Net for dense tiny object counting.