## 3 Experimental Results
### Training Results of the Mask RCNN Network  
The multitask loss curves of the Mask RCNN are shown in Figure 12. In Figure 12, the X-axis represents the number of training iterations, and the Y-axis represents the value of loss results. Four kinds of loss results are used to evaluate the performance of the training, which are total loss, class loss, mask loss, and bounding box regression loss, respectively. Total loss is the main loss function of the Mask RCNN, which is used to measure the overall performance of the network. Class loss is used to measure the prediction accuracy of the network to the target object category. It calculates the loss by comparing the difference between the prediction result and the label. Mask loss is used to measure the accuracy of the pixel-level segmentation of the target object. It calculates the loss by comparing the difference between the mask prediction results of the network and the real mask. Bounding  
Figure 11: Six types of switch machines.  
Figure 10: Segmentation description.  
box regression (bbox) loss is used to measure the prediction accuracy of the bounding box of the target. Based on the smooth L1 function, it calculates the loss by comparing the predicted results of bounding box with the real bounding box. The bbox loss is to minimize the difference between the predicted bounding box and the real bounding box. These kinds of the loss results are shown in Figure 12a-d, respectively. From Figure 12, it can be seen that a sudden decreasing occurs during the training process; however, the overall trend is downward. This is usually caused by the transition from the training head to the training full data or the high learning rate of the model. Although there are fluctuations in the four losses, the training losses gradually reduce. In other words, the training performance of the network gradually improves. The recognition rate of the training results are shown in Table 1.  
In Table 1, the recognition rate results are obtained from the six types of switch machines. It should note that each type of switch machine contains 50 images which are randomly selected from the existing dataset built from daily works for the recognition experiment and the following experiments for testing. The reason for selecting 50 images is that those images contain almost all the statuses of switch machines. A recognition is considered successful if and only if all six screws are recognized in the image.  
From Table 1, it can be seen that the recognition rate results of the six image groups are 98%, 100%, 100%, 98%, 100%, and 100%, respectively. For Type1, the feature of the screw region is similar to the surrounding region, and some screws cannot be recognized.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Recognition** & **Rate (\%)** \\ \hline Type1 & 49 & 98 \\ Type2 & 50 & 100 \\ Type3 & 50 & 100 \\ Type4 & 49 & 98 \\ Type5 & 50 & 100 \\ Type6 & 50 & 100 \\ Average & 49.67 & 99.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The successful recognition rate.  
Figure 12: Different kinds of loss curves: (**a**) loss; (**b**) class_loss; (**c**) mask_loss; and (**d**) bbox_loss.  
For Type4, the whole features of this switch machine are different from the other switch machines. While for the training process, the training number of Type4 is the same as the others, this may lead to the network not being able to recognize this kind of switch machine well. However, the average recognition rate is 99.33%, which indicates that the screw positions of six types of switch machines can be predicted with a high recognition rate. The subjective predicted positions of the screws are shown in Figure 13. From Figure 13, it can be seen that the predicted outlines of the screws are irregular, and the second segment needs to be analyzed.