# Microorganism Detection in Activated Sludge Microscopic Images Using Improved YOLO
## 4 Conclusions  
Activated sludge is a commonly used and highly efficient method for wastewater treatment. It effectively removes harmful substances from wastewater. However, the detection of microorganisms in activated sludge micrographs consumes significant time and human resources in daily operations. These microorganisms are crucial for wastewater treatment. Therefore, this study proposes a method for the detection of microorganisms in activated sludge micrographs. To address the detection of irregularly shaped microorganisms, we developed an improved YOLO model based on DCN and an attention mechanism to improve its detection capabilities.  
The summarization of our study is as follows: Firstly, we collected 650 activated sludge micrographs, including five object classes. Next, we preprocessed the micrograph dataset and applied DC-GAN data augmentation techniques to enhance object classes with fewer instances. The custom dataset consisted of 1000 micrographs after annotation.  
The results indicated that models trained on the augmented dataset exhibit superior performance compared to those trained on the original dataset, presenting the advantages of employing the DC-GAN augmentation technique. In the comparative experiment with the original YOLO model, the improved YOLO model based on DCN and attention mechanisms demonstrated better performance than the original YOLO model, particularly in the detection of irregularly shaped objects. In the final comparative experiments with existing object detection models, the improved YOLO model consistently achieved excellent performance, validating the effectiveness of our improvement approach.  
The performance of object detection models is affected by various factors, including the datasets, data preprocessing methods, network model structures, etc. Therefore, the model proposed in this paper may not be directly applicable to other application domains or datasets. However, the analytical methods used in this paper can provide insights for other object detection tasks in different fields.  
Conceptualization, Z.S. and Y.K.; methodology, Z.S. and Y.K.; software, Z.S.; validation, Z.S.; formal analysis, Z.S. and Y.K.; investigation, Z.S.; resources, Z.S. and Y.K.; data curation, Z.S.; writing--original draft preparation, Z.S. and Y.K.; writing--review and editing, Z.S. and Y.K.; visualization, Z.S.; supervision, Y.K.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Object Class** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** \\ \hline \multirow{4}{*}{R-CNN} & Granular sludges & 82.6\% & 81.0\% & \multirow{4}{*}{83.5\%} \\  & Granular Sludge Flocs & 80.1\% & 79.4\% & \\  & Filamentous Bacteria & 74.6\% & 73.8\% & \\  & Rotifers & 76.3\% & 77.2\% & \\  & Epistylis & 83.4\% & 82.7\% & \\ \hline \multirow{4}{*}{Faster R-CNN} & Granular sludges & 87.3\% & 86.9\% & \multirow{4}{*}{87.6\%} \\  & Granular Sludge Flocs & 85.4\% & 84.3\% & \\  & Filamentous Bacteria & 81.8\% & 80.9\% & \\  & Rotifers & 82.3\% & 82.7\% & \\  & Epistylis & 86.8\% & 84.2\% & \\ \hline \multirow{4}{*}{SSD} & Granular sludges & 90.8\% & 91.1\% & \multirow{4}{*}{91.3\%} \\  & Granular Sludge Flocs & 90.6\% & 90.2\% & \\ \cline{1-1}  & Filamentous Bacteria & 88.1\% & 89.0\% & \\ \cline{1-1}  & Rotifers & 87.3\% & 88.0\% & \\ \cline{1-1}  & Epistylis & 91.7\% & 90.2\% & \\ \hline \multirow{4}{*}{Improved YOLO} & Granular sludges & 93.6\% & 93.0\% & \multirow{4}{*}{93.7\%} \\  & Granular Sludge Flocs & 92.1\% & 93.2\% & \\ \cline{1-1}  & Filamentous Bacteria & 91.6\% & 92.1\% & \\ \cline{1-1}  & Rotifers & 91.3\% & 91.8\% & \\ \cline{1-1}  & Epistylis & 93.3\% & 93.9\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison table of results with existing methods.