## 3 Experimental Results
### Experimental Results of the Access Depth  
The experimental results of the access depth are shown in Table 3. From Table 3, it can be seen that the average error of the access depth between the proposed algorithm and the actual value is 0.41 mm, which indicates that the proposed algorithm has high accuracy in measuring the access depth between the static contact groups and the dynamic contact groups.  
The access depth errors of the six types of switch machines are shown in Figure 15. From Figure 15, it can see that the accurate results are 0.42 mm, 0.46 mm, 0.89 mm, 0.21 mm, 0.17 mm, and 0.3 mm, respectively, which means the proposed algorithm keeps a high level of accuracy in calculating the access depth for the different types of switch machines. It should be noted that the dataset mentioned in Section 3.1 with 2400 images is used for training the networks of [PERSON] et al. [12] and [PERSON]. As most recent studies have focused on object detection, few of them present the applications for switch machines. [PERSON] et al. [12] attempt to detect the pillar of switch machines with two networks; this is a similar application to our work. Therefore, the algorithm proposed by [PERSON] et al. [12] is selected for the comparison. The experimental results of the proposed algorithm, [PERSON] et al. [12] and Yolo, are shown in Figure 16.  
In Figure 16, the access depth recognition rate indicates the successful obtainment of the access depth. From Figure 16, we can see that the proposed algorithm has the highest rate for successfully obtaining the access depth compared to the other algorithms which have 99.3%, 72%, and 75%, respectively. For the access depth accuracy, 98% of the test values of the proposed algorithm are kept within 1 mm. However, the methods proposed by [PERSON] et al. [12] and Yolo have lower accuracy rates for access depth within 1 mm, which are 67% and 68%, respectively. For testing accuracy in the range of 1 mm to 5 mm, the proposed algorithm, [PERSON] et al. [12], and Yolo attain 1.3%, 3%, and 3% accuracy, respectively. For the access depth accuracy of more than 5 mm, our proposed algorithm performs with 0%, and [PERSON] et al. [12] and Yolo perform with 2% and 4% error rates, respectively. It can be seen that the proposed algorithm provides better results than the other two algorithms. In  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Iou** & **Dice** \\ \hline Type1 & 0.961 & 0.98 \\ Type2 & 0.951 & 0.975 \\ Type3 & 0.972 & 0.97 \\ Type4 & 0.98 & 0.99 \\ Type5 & 0.982 & 0.991 \\ Type6 & 0.974 & 0.987 \\ Average & 0.97 & 0.982 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segmentation accuracy evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Proposed Algorithm** & **Actual Access Depth** & **Error** \\  & **(mm)** & **(mm)** & **(mm)** \\ \hline Type1 & 6.79 & 7.20 & 0.42 \\ Type2 & 6.84 & 7.30 & 0.46 \\ Type3 & 9.09 & 8.20 & 0.89 \\ Type4 & 6.71 & 6.50 & 0.21 \\ Type5 & 6.83 & 7.00 & 0.17 \\ Type6 & 7.20 & 6.90 & 0.30 \\ Average & 7.24 & 7.18 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental results of the access depth.  
terms of computational complexity, the proposed algorithm uses 0.3 s per image for testing, and [PERSON] et al. [12] and [PERSON] use 4 s and 2 s, respectively. Therefore, the computational complexity of the proposed algorithm can be seen as low.