# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 4 Experiment
### Experimental Results and Analysis  
#### 4.2.1 Comparison of Segmentation Models  
Several popular semantic segmentation networks were evaluated and compared with the U-Net architecture for the purpose of image segmentation. The goal was to retain the characteristics of icing and complete the identification of the icing area. It is required that the model has strong robustness to icing segmentation. Table 5 lists the real label map of the original image of cable icing. Correspondingly, under the same training conditions, SegNet [22], DeepLab [23], and PSPNet [24] models have the best segmentation map for the icicles and surface ice layer. In the qualitative comparison, the segmentation result map of DeepLab contained many incomplete icicles and icing, and the segmentation effect of icicles could be better. Although it could segment the large surface ice layer well, it was unsuitable for tiny icicles; the holes and noise in the ice segmentation results of the PSPNet were not wholly compared with the real label, and the edge of the icicles was blurred in the detection. This incomplete segmentation feature would lead to poor performance in predicting the ice grade of the cable. SegNet and PSPNet could be stronger in the segmentation of icing and icicles, and there are severe drawbacks to insufficient segmentation ability. Compared with the above three popular networks, the U-Net image segmentation ability used in this paper was the best, manifested in the minor error segmentation content (red and green marks in the graph). In Table 6, the original image of the right side camera of the example had a large icicles area, which is an excellent test for the model segmentation ability. The prediction results show that U-Net still had good segmentation ability for such samples. Therefore, U-Net makes up for the over-segmentation of DeepLab and the under-segmentation of SegNet and PSPNet. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Category** & **Accuracy** & **IoU** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{DeepLab} & background & 93.48 & 87.04 & 92.97 & 93.17 & 93.07 \\  & surface ice & 96.09 & 91.84 & 96.09 & 95.40 & 95.74 \\  & icicles & 96.29 & 46.50 & 61.97 & 65.07 & 63.48 \\ \hline \multirow{3}{*}{PSPNet} & background & 93.15 & 86.51 & 92.08 & 93.46 & 92.77 \\  & surface ice & 95.63 & 91.31 & 95.45 & 95.46 & 95.46 \\  & icicles & 96.40 & 43.75 & 65.93 & 56.54 & 60.87 \\ \hline \multirow{3}{*}{SegNet} & background & 92.76 & 85.85 & 91.40 & 93.40 & 92.39 \\  & surface ice & 95.10 & 90.27 & 95.22 & 94.55 & 94.88 \\  & icicles & 96.28 & 42.54 & 64.52 & 55.53 & 59.69 \\ \hline \multirow{3}{*}{U-Net} & background & **95.41** & **90.62** & **95.80** & **94.37** & **95.08** \\  & surface ice & **97.08** & **94.11** & **96.95** & **96.98** & **96.97** \\ \cline{1-1}  & icicles & **97.48** & **61.58** & **71.59** & **81.49** & **76.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Segmentation results of ice-covered stay cables (%). In addition to visual comparison, Table 5 shows the performance of each model on the test set in terms of background category, surface ice category, and icicles category in quantitative comparison. We used Accuracy, IoU, Precision, Recall, and F1 value evaluation indicators to evaluate U-Net over DeepLab, PSPNet and SegNet models. Regarding segmentation accuracy, the U-Net used in this paper achieved better performance parameters than the comparison model. The Accuracy, IoU, Precision, Recall, and F1 values of U-Net for the surface ice category were 97.08%, 94.11%, 96.95%, 96.98%, and 96.97%, respectively. The Accuracy, IoU, Precision, Recall, and F1 values of the icicles category were 97.48%, 61.58%, 71.59%, 81.49%, and 76.22%, respectively. This is due to the U-Net's U-shaped encoding-decoding structure, which has rich feature representation capabilities.