## 5 Experiment and Results Description
### Experiment and Result Analysis
#### 5.4.2 Experiment with the New Network Architecture
To demonstrate the effectiveness of the method proposed in this article, commonly used network architectures were used instead of modified networks for specific tasks. Figure 11: Three groups (**A–C**) of segmentation results. In the table, \(\xi\), represents one of the four networks, which is LinkNet, ResNet, U-Net, or DeepLab. The \(\xi\)-row shows the test set segmentation results of the original network after training with the original database without displacement and rotation. The \(\xi\)-DR row shows the segmentation results of the test set when the original network was trained on the enhanced dataset, which included random displacements within 20 pixels and random rotations within 20 degrees. The PD-\(\xi\)-No row presents the test set segmentation results of PDNet modified on the basis of the \(\xi\) network after training. The PD-\(\xi\)-DR row displays the test set segmentation results of PDNet modified based on the \(\xi\), network when images with random displacements within 20 pixels and random rotations within 20 degrees were added to the training set. The precision and recall are both positive indicators. Equations (9) and (10) indicate that the precision focuses on comparing the whole positive area, while the recall focuses on comparing the correctly segmented area. Regardless of which region is compared, the precision and recall have the worst performance with the unmodified network after training on the enhanced data, with the values greater than 80%, while the results of the other networks are more than 90%. Compared with the precision and recall of the original network's segmentation on the databases without displacement or rotation, the results of the modified network are not significantly different than those of the original database or the rotated and displaced databases, indicating that the modified network has linear invariance. The segmentation should be controlled as much as possible to ensure that the region remains within the ground truth area with as similar a shape as possible. The positive samples are not sufficient to illustrate the network segmentation accuracy. According to Equation (12), the MIoU is a better metric for assessing accuracy. By comparing the IoU of each network, we find that the results of the PDNet-improved networks are quite different than those of the original network. Compared with the original network, the MIoU (\(\xi\)-No and PD-\(\xi\)-No) of the original data segmentation by the modified PDNet is approximately the same as that of the unmodified network, with values of approximately 1%. However, compared with the database after rotation and displacement, the IoUs of the data-enhanced network and the transformed networks (\(\xi\)-DR and PD-\(\xi\)-DR) differ substantially. The difference between the data-enhanced and transformed networks is at least 5%, with the largest difference being 20%. The differences are 5%, 6%, 20%, and 13%. After adding the position information to the original network, PDNet's performance on the rotated and displaced data returns to the original high performance. By comparing the