## 4 Experiment Results and Analysis
### Detection Results and Analysis  
The detection results of SEM-RCNN on EMDS-6 is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IoU** & \multirow{2}{*}{**Mean IoU**} & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} \\
**Threshold** & & & & & \\ \hline
0.1 & 0.633 & 0.428 & 0.422 & 0.461 & 0.441 \\
0.2 & 0.724 & 0.500 & 0.505 & 0.538 & 0.524 \\
0.3 & 0.7  
The loss curves while training and validation are shown in Figure 13. It shows that the bounding box loss and classification loss curves of the proposed SEM-RCNN can converge steadily and quickly, which indicates that the proposed SEM-RCNN model has satisfactory performance on bounding box regression and object classification tasks.  
In order to show more intuitively how the SEM-RCNN compares with other detectors, we compare them in the form of a table in the following. In Table 3, we compared the average detection indices (with 5-fold cross validation) of SEM-RCNN and Mask RCNN on EMDS-6 when combined with ResNet-50 and ResNet-101.  
Figure 12: The detection results of SEM-RCNN on EMDS-6. The color boxes indicate the positions predicted by SEM-RCNN, for which its captions indicate the predicted categories and the probabilities.  
Figure 13: The loss curves of SEM-RCNN while training and validation on EMDS-6.  
From Table 3, we can find that SEM-RCNN proposed in this paper can achieve better detection performance than Mask RCNN both in single-object and multi-object for EM detection. Overall, the models based on the backbone of ResNet-101 and SE-ResNet-101 perform better than those based on ResNet-50 and SE-ResNet. Compared with the original ResNet-based Mask RCNN, the performance increment of SEM-RCNN based on deeper SE-ResNet is much better than the Mask RCNN based on deeper ResNet both for mAP, precision, recall, and F1-score. Moreover, the mAP of the proposed SEM-RCNN achieves 0.511, which is much better than the result of Mask RCNN. The confusion matrix of the proposed SEM-RCNN is shown in Figure 14, showing the classification performance of the proposed model.  
From Figure 14, most of the test images can be classified correctly. However, there still exists some mis-detected images. For example, 5 images of _Codosiga_ are wrongly classified as _Epistylis_, showing the large error rate of the proposed model. By referring to the images of the two EMs in Figure 15, we find that the EMs have similar morphological features, and both are clustered microorganisms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**Mask RCNN**} & \multicolumn{2}{c}{**SEM-RCNN**} \\ \hline Backbone & ResNet-50 & ResNet-101 & SE-ResNet-50 & SE-ResNet-101 \\ mAP & 0.440 & 0.488 & 0.450 & 0.511 \\ precision & 0.434 & 0.485 & 0.425 & 0.509 \\ recall & 0.458 & 0.511 & 0.451 & 0.550 \\ F1-score & 0.446 & 0.498 & 0.455 & 0.526 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average detection indices (with 5-fold cross validation) of SEM-RCNN and Mask RCNN.  
Figure 14: The confusion matrix of the detection results of SEM-RCNN on EMDS-6 (with 5-fold cross validation). Deeper color indicates the higher classification probability.  
Moreover, by reviewing Figure 14, we find that _Stentor_ is easily classified as _Paramecium_, an example is shown in Figure 16. Most EMs are colorless and transparent, so the morphological feature determines the classification result. However, though the EMs are in variance shapes, they may perform similar shapes at different growth phases. On the other hand, the quantity and quality of the dataset will have a greater impact on the model's performance. However, the satisfactory EM dataset is relatively difficult to obtain due to some objective reasons, such as the impurities in the acquisition environment, uneven natural light, and other adverse factors. So the model still cannot classify similar EMs accurately due to the small dataset.