## 4 Discussion
### The Performance of Yolov3-Tiny, Yolov3, and Yolov4  
As shown in Figure 11, the mAP of the Yolov3-Tiny and Yolov3 models is 77.68% and 90.92%, respectively; the detection time is 2.68 and 16.52 milliseconds (msec), respectively. The mAP of the Yolov4 model is 91.99%, with the detection time of 28.41 msec, as shown in Table 11. Figure 11 Details for the mAP calculation of the Yolov3-Tiny are shown in Table 12. Details for the AP of each category by Yolov3-Tiny and Yolov3 and Yolov4 are as shown in Figures 12-14, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Categories** & **AP(\%)** \\ \hline Head of female dogs & 54.19 \\ Body of female dogs & 88.43 \\ Surgical position of female dogs & 76.92 \\ Surgical organs of female dogs & 77.28 \\ Head of male dogs & 60 \\ Body of male dogs & 63.75 \\ Surgical position of male dogs & 60 \\ Surgical organs male dogs & 31.82 \\ Head of female cats & 93.99 \\ Body of female cats & 100 \\ Surgical position of female cats & 100 \\ Surgical organs of female cats & 78.72 \\ Head of male cats & 95.08 \\ Body of male cats & 100 \\ Surgical position of male cats & 100 \\ Surgical organs male cats & 62.8 \\ mAP (\%) & 77.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Yolov3-Tiny AP/mAP. Figure 11: Model efficiency mAP(%). Figure 12: Yolov3-Tiny AP of each category. Figure 13: Yolov3 AP of each category. The Performance and Confidence Thresholding of Majority Voting Multi-Classification by Yolov3-Tiny, Yolov3, and Yolov4  
The mean accuracy is obtained by averaging the accuracy of each class. We use average accuracy because its measurement deviation is smaller than the accuracy in the case of unbalanced datasets. In the training phase, when using an unbalanced dataset, the weights may be biased toward larger groups, so random test images are more likely to be assigned to larger groups. Therefore, in this study, 96 test samples containing 11 male dogs, 19 female dogs, 29 male cats, and 37 female cats were deployed for experimental validation for the proposed model. In Tables 13-15, the accuracy is the ratio of all predicted correct samples to the total samples. Taking Table 13 as an example, the sum of 70 samples judged to be correct by the majority voting system are 2 male dogs, 7 female dogs, 27 male cats, and 34 female cats. With the \"other\" by 9, 12, 2, and 3 samples, respectively, there are 96 total samples. Thus, 0.73 is obtained by the ratio of 70 to 96. Individual accuracy is calculated by the ratio of the correct test samples to the total sample number in that specific class. An example for the male dog class, 2 divided by 11 equals 0.18. Mean accuracy is the average of the total sum of each individual accuracy. An example for the mean accuracy (=0.6) of the male dog class is the sum of 0.18, 0.37, 0.93, and 0.92, followed by the division of 4. In Figures 15 and 16, the threshold value parameter x is set as the majority voting value, which is used to determine whether to give a classification category; if the majority voting operation is lower than x, then the output is \"other\". The number of manual test samples is the number judged as \"other\" by the majority voting algorithm. More details will be discussed in the Results Section. Figure 14: Yolov4 AP of each category. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Male Dog** & **Female Dog** & **Male Cat** & **Female Cat** & **Accuracy** & **Mean Accuracy** \\ \hline Male dog & 2 & & & & 0.73 & 0.6 \\ Female dog & & 7 & & & & \\ Male cat & & & 27 & & & \\ Female cat & & & 34 & & & \\ “Other” & 9 & 12 & 2 & 3 & & \\ Individual accuracy & 0.18 & 0.37 & 0.93 & 0.92 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The performance of Yolov3-Tiny majority voting for multi-classification.