## 4 Discussion
### Analysis of the Best MIoU, Eval Point Avg Class IoU, and IoU Results for Each Class  
The overlap degree IoU is called the intersection-to-merge ratio, and as the name suggests, IoU calculates the ratio of the intersection and the merge of the \"predicted range A\" and the \"true range B\". It has been used in semantic segmentation as a standard measure of average accuracy. The intersection-to-merge ratio is not only used in semantic segmentation but is also one of the commonly used metrics in the direction of target detection, etc. The calculation formula can be expressed as:  
\[IoU=\frac{A\cap B}{A\cup B} \tag{1}\]  
The mean intersection and ratio mIoU (mean IoU) are obtained by accumulating the IoU for each class that is calculated based on class and then averaging it, which is the global-based evaluation. The calculation formula can be expressed as follows:  
\[MIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^{ k}p_{ji}-p_{ii}} \tag{2}\]  
where p\({}_{\bar{\mathrm{ij}}}\) denotes the number of true values for i that are predicted to be j. K + 1 is the number of categories (including the empty category). p\({}_{\bar{\mathrm{ii}}}\) is the true number. p\({}_{\bar{\mathrm{ij}}}\) and p\({}_{\bar{\mathrm{ji}}}\) denote the false positive and false negative, respectively.  
From the analysis in Figure 10, we can see that the best mIoU showed a steady and slow increase after the 10 th round and reached 0.6+. The IoU for the eight classes fluctuated, but the overall trend was still gradually increasing, indicating that the learning process was still going on normally.  
According to the label color of each class, it can be seen that the IoU of entrance/exit and track space in the subway station is higher. The morphology and spatial location of the entrance/exit and track space are relatively stable and relatively independent. Thus, the entrance/exit is not affected by other functional partitions, and the track partition is not affected by traffic space, which is less interspersed and easy for the model to learn the features. The IoU values of the pay area, non-pay area, and platform level are in  
Figure 10: Best mIoU, eval_point_avg_class_IoU, and IOU for each class.  
the middle; the reason for this is that their spatial location is relatively stable. While the spatial morphology changes more, more data training is needed to improve the IoU, and the platform level space also belongs to the pay area space. The three classes with the lowest IoU values are vertical traffic space, accessible elevators, and escalators and stairs. One important reason for the low IoU values is that they span high floors and have more complex spatial characteristics and also because the location of vertical traffic spaces is not stable and not easily understood by the program. These differences are attributed to the different morphological and spatial location characteristics of each class, e.g., the IoU of the entrance/exit and track spaces is close to 0.8.  
The analysis shows that the IoU for each functional space is closely related to its own characteristics, and the IoU from high to low are the entrance, track, non-paying area, paying area, platform level, barrier-free elevator, and stairs and escalator. The ones interspersed with other partitions are in the middle, such as the paid area, non-paid area, and platform level, and the three non-paid areas have the lowest possibility of being spatially crossed, so the IoU is also the highest among these three. The last one is the traffic space, which is almost bound to be crossed with other spaces, so its value is lower. Since the internal process of the neural network cannot be accurately grasped, the authors believe that the lower IoU for the three traffic cores may also be caused by the fact that their spatial extent is more specific to the z-axis.