## 4 Experiments and Results
### Qualitative Analysis  
For the qualitative assessment of the models' performance, several examples of correct detections and common failure cases were reviewed. The correct detections involved accurately identifying chickens amidst varying backgrounds, lighting conditions, and occlusions. These successes can be attributed to the algorithm's design: the stationary object detection module effectively identifies regions with no motion over time, while the classification module, leveraging knowledge distillation from EfficientNet-B0 to DeiT-Tiny, extracts detailed features such as texture, posture, and orientation. The dynamic frame  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Inference Time** & **Model** \\   EfficientNet-B0 & 99.3\% & 98.8\% & 99.1\% & 98.9\% & 15 & 28.4 \\ EfficientNet-B1 & 99.1\% & 98.6\% & 98.9\% & 98.7\% & 18 & 34.6 \\ ResNet-50 & 98.9\% & 98.4\% & 98.7\% & 98.5\% & 20 & 25.6 \\ ResNet-152 & 99.0\% & 98.5\% & 98.8\% & 98.6\% & 25 & 58.2 \\ DenseNet169 & 98.8\% & 98.2\% & 98.5\% & 98.3\% & 22 & 26.7 \\ DenseNet201 & 98.7\% & 97.9\% & 98.2\% & 98.0\% & 24 & 32.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics for various teacher models.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **DeiT-Tiny** & **DeiT-Small** & **DeiT-Base** \\ \hline Validation accuracy & 98.7\% & 99.0\% & 99.2\% \\ Average precision (AP) & 0.990 & 0.992 & 0.995 \\ Inference time (ms/frame) & 10 & 12 & 15 \\ Model size (MB) & 17.0 & 25.3 & 60.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different DeiT models.  
capture intervals and object size filter further enhance this process by adapting to the age of the chickens, minimizing false positives.  
Figure 7 showcases examples where the model successfully detected dead chickens under challenging conditions, including cluttered backgrounds and varying lighting. The high accuracy in these scenarios highlights the robustness of the knowledge-distilled DeiT model, which generalizes well across diverse conditions. The teacher-student distillation framework enables the DeiT model to retain the powerful feature extraction capabilities of EfficientNet-B0 while maintaining computational efficiency.  
Failure cases primarily included instances where shadows or similar-looking non-chicken objects, such as feeding plates or equipment, were mistakenly classified as chickens. These errors occur due to overlapping visual characteristics, such as shape or color similarity, or due to the placement of these objects within the frame. Additionally, extreme lighting conditions occasionally impacted the classifier's ability to extract distinguishing features, leading to false positives.  
To address these challenges, further improvements are proposed, such as incorporating a temporal attention mechanism to analyze motion patterns over time and adding preprocessing steps like shadow detection and removal. These enhancements could reduce false positives by refining the distinction between chickens and non-chicken objects.  
Despite these occasional misclassifications, the mistakes were infrequent, underscoring the models' high discriminative capabilities and overall reliability. The proposed algorithm balances accuracy and computational efficiency, making it a robust solution for real-time applications in high-density broiler farm environments.  
Figure 7: Examples of correct detections in (**a,b**) and common failure cases in (**c**). The first column contains the images of stationary object detection (within green box), and the second column contains the images of the confirmed dead chickens (within red box).