# Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
## 3 Application of the Proposed Approach to Insulator Detection
### The Analysis Results for All the Applied Convolutional Neural Networks  
The operation results of the selected convolutional neural networks were evaluated based on three parameters: precision, recall, and average precision. The precision parameter shows how often the network generates an incorrect prediction. The precision parameter is described by the relationship of true positive predictions resulting from the network (correctly detected insulators) and false positives (number of wrong predictions) generated by the network. In turn, the recall parameter shows how many objects were detected from all the tagged objects. It is crucial to assess the accuracy of the prediction of the region of interest generated by the network. The IoU (intersection over union) parameter was used to assess this accuracy. The IoU parameter describes the relationship between overlap area which is the sum of the area of the region delimited by the network and tagged manually in the picture and union area which is the product of the area of the region delimited by the network and the region hand-tagged in the picture. In the analysis of network operation, the key parameter was the average precision parameter, as well as the average precision curve, on the basis of which this parameter was determined. This curve was drawn based on previously determined precision and recall parameters for each single object detection. The shape and position of the average precision (AP) curve allowed assessing the accuracy of the network operation. For example, if the AP curve is more horizontal and located higher on the \(y\)-axis, the network is more accurate, while, as it reaches further on the \(x\)-axis, the number of objects it manages to detect correctly is increased. Ideally, it should be a straight line at the level of \(y=1\) reaching up to the value of \(x=1\). On this basis, the average precision parameter was calculated. Table 2 presents the results of analyses for selected neural networks trained using 15, 30, and 60 frames of film depicting the flight along the high-voltage line. Table 2 contains  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name of the CNN** & **Abbreviation** & **CNN Model** & **Main Features** \\ \hline \hline \multirow{7}{*}{You Only Look Once v3} & \multirow{7}{*}{YOLO v3} & \multirow{7}{*}{\(\bullet\)} & Can operate with the speed of 45 frames per second [16] \\  & & & The single convolutional network predicts the bounding boxes and the class probabilities for these boxes [15]. & The limitation of the YOLO algorithm is that it struggles with small objects within the image (due to the spatial constraints of the algorithm) [17] \\  & & & & The tasks of object localization and classification are done in a single forward pass of the network [22] \\  & & & & The network employs inception V3 as the backbone [18, 19, 20, 21] \\  & & & & \\  & & & &  
values of average precision, precision, and recall parameters calculated for three different established IoU values (i.e., 0.25, 0.5, and 0.75).  
As can be seen in Table 2, a change in the value of the IoU parameter had a significant impact on the analysis results. For further analysis, it was assumed that the IoU level equal to 0.5 was sufficient to conclude that the insulator was correctly identified in the image. For an IoU of 0.5, it is clear that the networks Faster R-CNN and R-FCN had by far the highest efficiency. The recall parameter was particularly important here, which had a value of as much as 0.7925 for the R-FCN/ResNet network trained on 60 frames for the IoU of 0.5. This means that, in this case, the network was able to identify almost 80% of the insulators correctly. Given that the networks Faster R-CNN and R-FCN achieved much better results than the other algorithms, they were selected for further, more detailed analyses.