# 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 first step in the further analysis was to verify how the increase in the number of video frames used for training affected the quality of network operation. For this purpose, both networks were re-trained using five, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60 frames. Figure 3 presents the average precision curves of the Faster R-CNN model for the IoU of 0.5, which demonstrate the results of the network operation on the test dataset for different numbers of frames that were involved in the training process. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Network Model** & \begin{tabular}{c} **Number** \\ **of** \\ **Frames** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Precision** \\ **IOU =** \\ **0.25** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **IOU =** \\ **0.25** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **IOU =** \\ **0.25** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Precision** \\ **IOU =** \\ **0.5** \\ \end{tabular} & \
\begin{tabular}{c} **Precision** \\ **IOU =** \\ **0.5** \\ \end{tabular} &
\begin{tabular}{c} **Recall** \\ **IOU =** \\ **0.75** \\ \end{tabular} \\ \hline Faster & 15 & 0.6156 & 0.8846 & 0.6137 & 0.3952 & 0.6537 & 0.4535 & 0.1064 & 0.1509 & 0.1047 \\ R-CNN & 30 & 0.7044 & 0.8989 & 0.7223 & 0.4723 & 0.7000 & 0.5625 & 0.0670 & 0.1731 & 0.1391 \\ Faster & 60 & 0.8043 & 0.9274 & 0.8328 & 0.6718 & 0.7964 & 0.7152 & 0.1584 & 0.2671 & 0.2398 \\ R-CNN & 15 & 0.7069 & 0.9123 & 0.7027 & 0.4805 & 0.7110 & 0.5477 & 0.1110 & 0.1668 & 0.1285 \\ R-FCN & 30 & 0.8015 & 0.9272 & 0.8461 & 0.5580 & 0.7354 & 0.6711 & 0.1178 & 0.2098 & 0.1914 \\ R-FCN & 60 & 0.8948 & 0.9515 & 0.9266 & 0.6598 & 0.8139 & 0.7926 & 0.1589 & 0.2720 & 0.2648 \\ YOLO v3 & 15 & 0.2651 & 0.9324 & 0.2965 & 0.1418 & 0.5258 & 0.1672 & 0.0056 & 0.0590 & 0.0188 \\ YOLO v3 & 30 & 0.3493 & 0.9208 & 0.3727 & 0.1127 & 0.4797 & 0.1941 & 0.0114 & 0.0531 & 0.0215 \\ YOLO v3 & 60 & 0.4445 & 0.9427 & 0.4500 & 0.2000 & 0.5483 & 0.2617 & 0.0909 & 0.0687 & 0.0328 \\ SSD & 15 & 0.3589 & 0.9479 & 0.3910 & 0.2285 & 0.6771 & 0.2793 & 0.0909 & 0.1752 & 0.0723 \\ Inception SSD & 30 & 0.4486 & 0.9528 & 0.4813 & 0.3096 & 0.7301 & 0.3688 & 0.0331 & 0.1570 & 0.0793 \\ Inception SSD & 60 & 0.5365 & 0.9589 & 0.5465 & 0.3944 & 0.7615 & 0.4340 & 0.1199 & 0.2557 & 0.1457 \\ SSD & 15 & 0.3520 & 0.9286 & 0.3555 & 0.2238 & 0.6224 & 0.2383 & 0.0273 & 0.1327 & 0.0508 \\ MobileNet SSD & 30 & 0.4344 & 0.9211 & 0.4238 & 0.2956 & 0.6842 & 0.3148 & 0.0303 & 0.1171 & 0.0539 \\ SSD & 60 & 0.5290 & 0.9225 & 0.5207 & 0.3014 & 0.6657 & 0.3758 & 0.0268 & 0.1488 & 0.0840 \\ MobileNet & & & & & & & & & & \\ SSD & 0.5290 & 0.9225 & 0.5207 & 0.3014 & 0.6657 & 0.3758 & 0.0268 & 0.1488 & 0.0840 \\ MobileNet & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the analyses results for all the applied convolutional neural networks. IOU—intersection over union. As shown in Figure 3, the number of training frames involved in the learning process had a significant impact on the detection quality of Faster R-CNN. Noticeably, the best result was obtained for the network trained on a dataset consisting of 60 training frames. It should also be noted that the networks trained with 25 and 35 frames were surprisingly good. Figure 4 presents the results obtained for the R-FCN model in similar conditions. As shown in Figure 4, the AP curves for the R-FCN model were flatter and reached further along the \(x\)-axis than those for the Faster R-CNN model. It can also be seen that the increase in the number of frames was not as crucial here as it was in the case of the Faster R-CNN model. In particular, this was visible for networks trained for more than 15 frames. The slope of AP curves did not change as significantly here either. In the final stage of the analysis, the relationship between the increase in the number of training frames and the AP parameter value was examined.