# Broiler Behavior Detection and Tracking Method Based on Lightweight Transformer
## 3 Results and Discussion
### DDConv and MSDCC Ablation Experiments  
It has long been established that the performance of a model can be enhanced by increasing its depth, width, input resolution, bottleneck rate, and group width. [PERSON] et al. (2022) [36], demonstrated that expanding the effective receptive field of a convolutional neural network (CNN) by adding convolutional kernels can also lead to improved model performance. However, this approach results in an exponential increase in the number of parameters and floating-point operations. [PERSON] et al. (2017) and [PERSON] et al. (2017) [37, 38], proposed that this drawback can be significantly mitigated through the application of depthwise convolution (DW). Building on this concept, we propose DDConv in this paper, which expands the receptive field of the model by applying depthwise convolution (DW) with a 5 \(\times\) 5 kernel twice after channel splitting. Figure 10 presents a schematic representation of the receptive field before and after model enhancement. In each image, the extent of the dark region (green) indicates the area influenced by the pixel on the output; specifically, a darker region signifies that the corresponding input pixel has a greater contribution to the network output. Consequently, a more extensively distributed dark area reflects a larger effective receptive field (ERF) [39]. Therefore, DDConv demonstrates a larger receptive field in comparison to the original convolution. Additionally, Table 6 shows the high contribution area ratio for different t thresholds before and after model improvement. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**Test Set A**} & \multicolumn{2}{c}{**Test Set B**} & \multirow{2}{*}{**Params (M)**} & \multirow{2}{*}{**FLOPs (G)**} \\ \cline{2-2} \cline{5-6}  & **mAP50** & **mAP50-95** & & & & \\ \hline ResNet18 & 98.6 & 84.9 & 31.2 & 20.1 & 20 & 60 \\ ResNet34 & 99.4 & 88.6 & 31.0 & 18.4 & 31 & 92 \\ ResNet50 & 99.2 & 89.0 & 37.7 & 23.5 & 36 & 136 \\ HGNetv2 & 99.3 & 88.4 & 32.2 & 19.1 & 32 & 110 \\
**FasterNet** & **98.3** & **84.3** & **27.6** & **17.4** & **10.8** & **28.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of models under different backbones. The threshold t represents the percentage of the highest contribution scores considered for calculating the high-contribution area ratio r. For example, a threshold of t = 20% means that only the pixels with the top 20% contribution scores are included when determining the area ratio r. In Table 6, the effective receptive field of the optimized downsampling layer demonstrates a significant enhancement at low t values (20% and 30%), achieving 2.3% and 4.4%, respectively. This represents a substantial improvement compared to the original downsampling layer's performance of 1% and 2.3%. These results indicate that the new module enhances the model's capacity to capture local information. Furthermore, comparable performance (10.4%) is sustained for medium and high t values (50%), effectively preserving robust global feature extraction capabilities. Although there is a slight reduction in receptive fields at extremely high t values (99%), the model exhibits a more balanced capacity to capture varying levels of information, which is crucial for chicken detection scenarios that require simultaneous processing of local details and global semantic information. Therefore, we selected DDonv as the downsampling layer and further optimized the model by designing MSDCC. Table 7 demonstrates that the introduction of MSDCC results in lower FLOPs and a reduced number of parameters. On test set A and test set B, the mAP50 and mAP95 metrics of the proposed model outperform those of other models. This not only indicates the high accuracy and robustness of the proposed model on these test sets but also further validates the effectiveness of DDConv and MSDCC in enhancing the model's performance.