# FE-P Net: An Image-Enhanced Parallel Density Estimation Network for Meat Duck Counting
## 3 Results
### Ablation Experiments  
Ablation experiments were conducted to evaluate the impact of the various substructures on model performance. Our method includes two branches and a detail enhancement module. Several ablation studies were designed to validate their effectiveness, with the results shown in Table 2.  
Without the convolution branch, the model's MAE increased to 8.44 and the MSE to 10.29. This increase is attributed to the failure of the density-enhanced Transformer encoder and the counting loss strategy used by the convolution branch, rendering the loss strategy ineffective. Adding the convolution branch significantly improved performance, reducing MAE to 4.15 and MSE to 4.95. Incorporating the LPEM module further enhanced feature representation, mitigating low-resolution image issues and improving object localization and edge detection, which lowered the MSE to 3.56. Finally, adding the SCCONV module to the convolution branch eliminated feature redundancy, allowing the Transformer encoder to integrate more precise features and reduce noise, ultimately lowering the error to 3.01. These experiments demonstrate the positive contributions of each module and validate the superiority of the dual-branch structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**LPEM** & **CNN Branch** & **SCCONV** & **MAE** & **MSE** \\ \hline \(\times\) & \(\times\) & \(\times\) & 8.44 & 10.29 \\ \(\times\) & \(\checkmark\) & \(\times\) & 4.15 & 4.95 \\ \(\checkmark\) & \(\checkmark\) & \(\times\) & 3.56 & 4.32 \\ \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & **3.01** & **4.15** \\ \hline \hline \end{tabular} A \(\checkmark\) symbol indicates that the module has been added and a \(\times\) symbol indicates that the module has not been added. The bold values represent the optimal performance.  
\end{table}
Table 2: Ablation experiments for the different modules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **MAE** & **MSE** & **Accuracy** \\ \hline CCST & 3.89 & 4.69 & 95.41\% \\ MAN & 4.18 & 5.32 & 95.08\% \\ MFF & 5.93 & 7.16 & 93.01\% \\ TransCrowd & 3.95 & 5.03 & 95.35\% \\ P2 PNet & 4.64 & 5.11 & 94.53\% \\ IOC & 4.15 & 4.95 & 95.11\% \\ FE-P & **3.01** & **4.21** & **96.46\%** \\ \hline \hline \end{tabular} The bold values represent the optimal performance.  
\end{table}
Table 1: Comparison of counting accuracy among models.  
Figure 9: Results of different segmentation models.  
The encoder plays a crucial role in feature extraction, generating feature maps from input images to support subsequent detection tasks. Selecting an appropriate feature extraction network can significantly enhance model performance. This section evaluates the use of different feature extraction networks as the backbone encoder through a series of experiments, with the results shown in Table 3.  
The experimental results indicate that using ResNet-50 [34] as the backbone encoder achieves the best performance. Shallower networks such as ResNet-18 and ResNet-34 offer faster inference but provide less comprehensive feature extraction, leading to reduced accuracy. In contrast, using ResNet-101 as the encoder results in over-extraction due to its deeper layers, which also decreases accuracy.