# Broiler Behavior Detection and Tracking Method Based on Lightweight Transformer
## 3 Results and Discussion
### Fine-Tuning Experiment  
Although FCBD-DETR demonstrates superior performance compared to other models, particularly in terms of operational efficiency, it still exhibits certain limitations. Specifically, the detection accuracy of the proposed model on test set B is lower than that on test set A. To address the practical application requirements, we conducted additional boosting experiments on test set B to enhance model performance. [PERSON] et al. (2021) [40], proposed a method based on fine-tuning pre-trained weights, which can improve the model's adaptability to downstream tasks. Consequently, fine-tuning experiments were performed on test set B to further augment the model's detection capabilities. The objective of the fine-tuning experiment was to determine whether optimal model weights can be achieved by utilizing existing pre-trained weights to enhance the object detection of chicken behaviors, while minimizing the usage of new training data and reducing the number of training cycles. The specific methodology is as follows: Initially, from the 400 images in test set B, 50 images are randomly selected to form the fine-tuning validation set, while another 50 images constitute the fine-tuning test set. The remaining 300 images are divided into 3 fine-tuning training sets, containing 100, 200, and 300 images, respectively. Subsequently, each training set is trained for 100, 150, and 200 epochs, utilizing the pre-trained weights accordingly. Finally, the fine-tuning test set is employed to evaluate the model's detection performance under varying amounts of training data and training durations. Figure 11 illustrates the results of fine-tuning experiments conducted using RT-DETR and FCBD-DETR. The experimental findings indicate that, in the absence of pre-trained weights, our model demonstrates a significant enhancement in mAP at both 100 and 150 iterations. Notably, when constrained to 100 training images, FCBD-DETR exhibits considerable capabilities in scenarios where pre-trained weights are utilized. After 200 iterations with 300 training images, the model achieves an mAP50 of 97.6%, in contrast to 95.0% when \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**mAP50(\%)**} & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**Params (M)**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{5-6}  & **Test Set A** & & & & \\ \hline SSD & 98.2 & 26.5 & 137.2 & 24.01 & 23.5 \\ Faster-RCNN & 98.6 & 30.5 & 200.9 & 136.75 & 11.8 \\ Yolov7 & 99.3 & 30.4 & 103.2 & 36.93 & 45.9 \\ Yolov8m & 99.1 & 27.4 & 78.7 & 25.8 & 57.6 \\ RT-DETR-R18 & 98.6 & 31.2 & 31.2 & 20.1 & 49.5 \\
**Our** & **99.4** & **31.6** & **18.3** & **8.9** & **68.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of performance between different models. pre-trained weights are not employed. The fine-tuning experiments reveal that the incorporation of pre-trained weights facilitates a faster convergence of FCBD-DETR, resulting in more favorable outcomes even when the quantity of newly labeled data is limited or the training duration is shortened, thereby significantly reducing the training cost. Figure 12 illustrates the visualization results of FCBD-DETR before and after fine-tuning on test set B. Prior to fine-tuning, the model exhibited issues such as missed detections, false detections, and inaccurate positioning. However, following fine-tuning, the model demonstrated significant improvements in new scenarios, effectively enhancing its ability to detect chicken behavior. Specifically, fine-tuning based on pre-trained weights enables the model to adapt quickly to new backgrounds at a low cost, thereby facilitating effective object-background differentiation. Consequently, FCBD-DETR shows considerable potential for adaptive and accurate detection of chicken behavior in complex breeding environments.