# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 4 Experiment
### Experiment Description
#### 4.1.2 Evaluation Indicators  
In order to verify the effectiveness of the proposed method based on the comparison between the prediction map and the label map, the confusion matrix was used to evaluate the model's performance. The confusion matrix evaluation model is a distinguishing method. TP represents the true positive sample, and the model also predicts the number of positive samples; FP represents the number of true negative samples, but the model predicts the number of positive samples; FN represents the true positive sample, but the model predicts the number of negative samples; TN represents the true negative sample, and the model also predicts the number of negative samples. Table 4 shows the construction method of the confusion matrix. This experiment involved the segmentation of icicles and surface ice and the estimation of icing degree level. Therefore, five indicators were applied in this experiment, namely, accuracy (A), recall (R), precision (P), F1 value, and intersection over union (IoU), to evaluate. The accuracy of the icing level estimation is evaluated by accuracy (A).  
Accuracy is the percentage of correctly identified pixels relative to all pixels, indicating the proportion of the model's correct prediction to the total prediction value, as shown in Equation (2).  
\[accuracy=\frac{TP+TN}{TP+TN+FN+FP} \tag{2}\]  
The proportion of the part of the accuracy rate that is positive, and indeed positive in all classifiers, is considered to be positive, and indicates the proportion of the model in all positive results in the prediction graph to predict correctly, as shown in Equation (3).  
\[pision=\frac{TP}{TP+FP} \tag{3}\]  
The recall rate refers to the proportion of the part of the classifier that is considered to be a positive class and is indeed a positive class in the proportion of all classifiers that are considered to be a positive class, indicating that the true value is a positive example of all the results. The model predicts the correct proportion, as shown in Equation (4).  
\[recall=\frac{TP}{TP+FN} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**True/Forecast** & **Icicles** & **Surface Ice** & **No Icing** \\ \hline Icicles & TP & FN & FN \\ Surface ice & FP & TP & TN \\ No icing & FP & FP & TP \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix construction method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level of Icing** & **Data Set** & **Train** & **Test** \\
**Degree** & 93 & 74 & 19 \\ First level & 521 & 416 & 105 \\ Second level & 886 & 708 & 178 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantity distribution of ResNet50 data set.  
The F1 value combines the precision rate and the recall rate, as shown in Equation (5).  
\[F1=2\times\frac{precision\times recall}{precision+recall} \tag{5}\]  
IoU is a standard metric for measuring the accuracy of image segmentation algorithms. The larger the IoU value, the higher the segmentation accuracy. The method for calculating IoU is to divide the intersection of the predicted region and the actual region by the union of the predicted region and the actual region, as shown in Equation (6).  
\[IOU=\frac{TP}{TP+FN+FP} \tag{6}\]