# FE-P Net: An Image-Enhanced Parallel Density Estimation Network for Meat Duck Counting
## 4 Conclusions  
In practical production environments, image quality is often low, and traditional bounding box annotation methods are labor-intensive. Our study introduces the FE-P Net architecture, which uses point annotations combined with a feature enhancement module to refine image features and improve detection accuracy. This approach offers a novel solution for enhancing poultry counting accuracy in farming scenarios. Unlike existing methods that struggle with low-resolution images, FE-P Net employs a dual-branch architecture to integrate local detail extraction and global semantic understanding. By refining edge details and enhancing feature representations while reducing redundancy in both the spatial and channel dimensions, FE-P Net effectively mitigates issues related to low resolution, blurred edges, and uneven lighting. This results in significant performance improvements, achieving 96.46% accuracy on the meat duck dataset. Comparative experiments confirm that FE-P Net outperforms other models on this dataset, successfully completing the counting task. Ablation studies further validate the contributions of each module to overall network performance. The findings have profound practical implications for poultry breeding. Deploying FE-P Net in real-time monitoring systems enables automated and accurate population counts, crucial for optimizing space utilization, improving animal welfare, and enhancing farm management efficiency. The use of point annotations significantly reduces the labor required for labeling, facilitating scalable deployment across large farming operations. However, there are areas for improvement. Eye-based point annotations require precise calibration, especially in highly crowded scenes with frequent occlusions. Further research should explore the model's robustness under varying lighting and environmental conditions. Combining FE-P Net with larger models could enhance performance and adaptability. In conclusion, this study advances the application of deep learning techniques in poultry breeding management. By addressing key challenges in image quality and object density, FE-P Net provides a promising solution for automating meat duck counting. As agriculture continues its digital transformation, innovations like ours will be essential in driving productivity and sustainability. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Encoder** & **MAE** & **MSE** & **Accuracy** \\ \hline ResNet-18 & 3.93 & 4.77 & 95.37\% \\ ResNet-34 & 3.76 & 4.63 & 95.57\% \\ ResNet-50 & **3.02** & **4.15** & **96.46\%** \\ ResNet-101 & 3.62 & 4.67 & 95.73\% \\ \hline \hline \end{tabular} The bold values represent the optimal performance.  
\end{table} 
Table 3: Ablation experiments for the different feature extraction networks. **Author Contributions:** Conceptualization, W.T. and H.Q. ; methodology, W.T. and M.L. ; software, W.T. ; validation, H.Q., X.C. and T.W. ; formal analysis, Y.E.X. ; investigation, X.C. and S.S.; resources, X.C. and S.S.; data curation, W.T. ; writing--original draft preparation, W.T. ; writing--review and editing, H.Q., M.L. and X.C. ; visualization, H.Q. ; supervision, X.C. ; project administration, X.C. and M.L. ; funding acquisition, Y.E.X. and S.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Key R&D Program of Xinjiang Uygur Autonomous Region (Grant No. 2022B02027-1 and 2023B02013-2), the Xinjiang Uygur Autonomous Region \"Tianchi Talent\" Introduction Program (Grant No. 20221100619) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20210408). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to they were collected in privately-owned poultry facilities.