# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 3 Application of the Proposed Approach to Insulator Detection
### The In-Depth Analysis Results for the Faster R-CNN and R-FCN Networks
The obtained results are shown in Figure 5, which also shows graphs and formulas of functions matched to the measurement data, together with calculated determination coefficients. Figure 4: Average precision curve for the R-FCN model trained on 5–60 frames. Figure 3: Average precision curve for the Faster R-CNN model trained on 5–60 frames. Figure 6 presents a comparison of average precision curves obtained for the R-FCN network additionally tuned in three considered cases with 15, 30, and 60 frames. As can be seen, the detection accuracy significantly improved; the precision parameter curves were closer to the line \(y=1\), but the recall parameter slightly deteriorated, as fewer insulators were found. Figure 7 presents an analogical situation to Figure 6, with the results obtained after testing the Faster R-CNN model. In this case, a significant improvement in detection accuracy can be observed, especially for 15 frames of the video, but there was also a decrease in the number of insulators found. In this case, the shape of the curves (30 and 60 frames) also changed; their end parts sloped downward. Accurately calculated parameters of average precision, precision, and recall are presented in Table 3.  
Average accuracy was not a clear indicator which, for the analyzed cases 15, 30 or 60, gave better results. It is worth noting that, as a result of the in-depth study, an improvement in precision was achieved in each of the cases. For the Faster R-CNN model, the improvement was respectively 23.19%, 15.84%, and 14.27%, and, for the R-FCN model, it was 21.44%, 20.31%, and 10.50%. On the other hand, the recall parameter for the Faster R-CNN model deteriorated respectively by 13.07%, 8.14%, and 9.29%, and, for the R-FCN model, it deteriorated respectively by 10.28%, 14.52%, and 12.57%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Network Model** & **Number of Frames** & \begin{tabular}{c} **Average** \\ **Precision** \\ **IOU = 0.5** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **IOU = 0.5** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **IOU = 0.5** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Precision** \\ **IOU = 0.5** \\ **Expanded** \\ **Training** \\ \end{tabular} & \
\begin{tabular}{c} **Precision** \\ **IOU = 0.5** \\ **Expanded** \\ **Training** \\ \end{tabular} &
\begin{tabular}{c} **Recall** \\ **IOU = 0.5** \\ **Expanded** \\ **Training** \\ \end{tabular} \\ \hline Faster & 15 & 0.4712 & 0.7251 & 0.5379 & 0.4384 & 0.8933 & 0.4676 \\ R-CNN & 30 & 0.5714 & 0.7574 & 0.6574 & 0.6106 & 0.8774 & 0.6039 \\ Faster & 60 & 0.6672 & 0.7929 & 0.7105 & 0.6221 & 0.9061 & 0.6445 \\ R-CNN & 15 & 0.4060 & 0.7424 & 0.4863 & 0.4368 & 0.9015 & 0.4363 \\ R-FCN & 30 & 0.4852 & 0.7650 & 0.5813 & 0.4402 & 0.9204 & 0.4969 \\ R-FCN & 60 & 0.6609 & 0.8206 & 0.7738 & 0.6109 & 0.9068 & 0.6766 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the results of the in-depth analysis including the calculated parameters of average precision, precision, and recall for the networks Faster R-CNN and R-FCN (15, 30, and 60 frames). Figure 7: The average precision curve for the Faster R-CNN model which was trained on a training dataset and additionally tuned in three considered cases with 15, 30, and 60 frames.