## 6 Conclusions and Further Research  
This study presents a robust and scalable system for automated dead chicken detection in broiler farms using CCTV footage. The proposed two-stage architecture, which combined stationary object detection with a knowledge-distilled vision transformer classifier, addressed the lack of annotated data for dead chickens. By employing traditional image processing to detect stationary regions first, followed by a deep learning classifier to confirm that the detected stationary object is indeed a chicken, the system significantly improved detection accuracy while maintaining computational efficiency.  
The system demonstrated excellent performance, with the EfficientNet-B0 teacher model achieving 99.3% validation accuracy and the DeiT-Tiny student model achieving 98.7%. Knowledge distillation enabled the student model to retain much of the teacher's feature extraction capabilities while reducing computational overhead, making the system well suited for real-time deployment in resource-constrained environments. The dynamic frame selection strategy further optimized resource usage by adjusting the monitoring frequency based on chicken age, ensuring efficient processing throughout the chickens' growth stages.  
The system successfully tackled real-world farm challenges, such as lighting variations, occlusions, cluttered backgrounds, chicken growth, and camera distortions, proving tobe both robust and reliable. This advancement minimizes the labor-intensive task of manual inspections and enhances farm management by providing an efficient, scalable monitoring solution.  
Future research will focus on expanding the dataset to include multiple farm environments, integrating a deep learning approach based on temporal attention mechanisms in video for the improved detection of subtle behavioral anomalies, and exploring automated removal systems for detected dead chickens. Temporal attention mechanisms will enable the model to analyze motion patterns across consecutive frames, improving its ability to detect behaviors and conditions indicative of chicken death. By capturing the temporal dependencies in the video data, this approach can refine the classification and reduce false positives or missed detections. These enhancements will further validate the system's generalizability and operational effectiveness, paving the way for widespread adoption in the poultry farming industry.  
Conceptualization, R.K. and J.L.; data curation, R.K. and J.L.; formal analysis, R.K.; funding acquisition, J.L.; investigation, R.K.; methodology, R.K. and J.L.; resources, J.L.; software, R.K. and W.W.; validation, R.K. and W.W.; visualization, R.K. and W.W.; writing--original draft, R.K.; writing--review and editing, W.W. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) and Korea Smart Farm R&D Foundation (KoSFarm) through the Smart Farm Innovation Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA), Ministry of Science and ICT (MSIT), and Rural Development Administration (RDA) (grant number: RS-2021-IP421024).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We would like to express our gratitude to our lab members [PERSON] and [PERSON] for their support in dataset curation.  
The authors declare no conflicts of interest.