## 4 Proposed Method
### PDNet
#### 4.2.2 Network Performance at Different Positions  
The raw image features learned by the convolutional network can be visualized by class activation maps (CAMs) for salient regions. To effectively determine the performance in salient regions, we compared the affected regions of the network under different weight masks of the same image. In order to analyze the correlation between the location information and the network during the research, the mask set was the standardized location map, and on this basis, the randomization test was carried out. The results are shown in Figure 4. The ground reality is mainly a gradient mask in the vertical and horizontal directions. The model was verified by these two masks, and it was proved that it can learn the absolute position information on the axis. In this paper, horizontal stripes and vertical stripes (HS and VS) were also created. A Gaussian filter is applied in the design of another type of image; the following figure shows the corresponding Gaussian distribution (G) center map.  
In this study, the gradient ground truth mask can be regarded as a random tag, and the corresponding input image and ground truth value are not correlated. The image content will not affect the extraction of position information, so any image data set can be selected according to the requirements during the research. In this paper, synthetic images were also generated to verify the proposed assumptions.  
The influence of the feature map on typical features shows that the mask has different effects at various positions in the image. Moreover, this influence is not linear. Although the effect is not linear, the results show a positive correlation.  
Figure 4: Gradient-like ground-truth position maps. (**a–e**) Different weight masks and their weighted graphs.  
To better evaluate the performance of these masks in each network layer, the convolution results of each layer were compared. Due to the limited margin of this article, only a few feature maps were compared for observation (shown in Figure 5).  
The feature maps of the above layers show that the retained features differ due to the different convolution kernels. Furthermore, the feature comparison charts indicate that the weights retained by the mask are not irregular. Important image information is preserved and iterated in the feature map, including horizontal and vertical information, texture information, and boundary information.  
A comparison of the feature maps of the same layer as in the vertical repetition map (HS) and horizontal repetition map (VS) shows that horizontal and vertical information is retained. Moreover, by comparing the feature maps of the same layer in the vertical image (H) and horizontal image (V), we can see that the texture information is preserved. Comparing the central Gaussian map (G) with the HS and VS maps shows that the boundary information is preserved. More complete information is retained in the larger feature map, and the peak information is preserved. Regardless of the direction of the image, the extracted features of similar image textures are comparable, and the features of the image direction are retained. The image features with changed orientations (HS and VS, H and V)  
Figure 5: Feature maps. (**a–e**) Different weight masks in the feature map of the middle layer.  
have symmetric relationships, even if they are not in the same layer. Due to the convolution and downsampling layers, the overall image information is difficult to preserve, and some position information and other relevant information is lost.