# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 4 Experiment
### Ablation and Baseline Comparison Experiments
#### 4.4.2 Comparison of Different Feature Migration Mechanisms  
For the edge-side migrated model, different feature migration configurations were designed by adjusting head.score.bias, head.score.bias+head.score.weight, and head.score.bias+head.score.weight+head.cls_loc.bias. The performance of each configuration was compared with that of the original model. The experimental results are shown in Table 6.  
As shown in the table, transferring the edge-side model's classification head components head.score.bias+head.score.weight can improve the original model's precision, recall, and average precision (AP) to varying degrees. For the A321 category, this transfer strategy achieves stable performance, with precision reaching 71.00% and AP reaching 53.71%, both exceeding the original model. For the A330 category, the precision reaches 39.62%, while in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Migration Category** & **Migration Model Site** & **Precision** & **Recall** & **AP** \\ \hline \multirow{4}{*}{A321} & Original model & 70.30\% & 60.68\% & 53.66\% \\  & head.score.bias & 70.30\% & 60.68\% & 52.71\% \\  & head.score.bias+head.score.weight & 71.00\% & 60.68\% & 53.71\% \\  & head.score.bias+head.score.weight+ & & & \\  & head.cls\_loc.weight+head.cls\_loc.bias & 31.28\% & 60.68\% & 34.87\% \\  & Original model & 32.26\% & 55.56\% & 52.38\% \\  & head.score.bias & 32.22\% & 55.56\% & 52.38\% \\ A330 & head.score.bias+head.score.weight & 39.62\% & 55.56\% & 22.02\% \\  & head.score.bias+head.score.weight+ & 36.85\% & 55.56\% & 20.47\% \\  & head.cls\_loc.weight+head.cls\_loc.bias & & & \\  & Original model & 44.12\% & 57.69\% & 37.85\% \\ Other-plane & head.score.bias+head.score.weight & 32.94\% & 59.40\% & 30.26\% \\  & head.score.bias+head.score.weight+ & & & \\  & head.cls\_loc.weight+head.cls\_loc.bias & 33.33\% & 59.83\% & 30.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of accuracy results for different fine-tuned components of the aircraft model on selected categories.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Model Used** & **Precision** & **Recall** & **AP** \\ \hline A321 & Original Model & 70.30\% & 60.68\% & 53.66\% \\ \hline \multirow{4}{*}{A330} & Without & \multirow{2}{*}{67.44\%} & \multirow{2}{*}{55.77\%} & \multirow{2}{*}{47.17\%} \\  & Reinforcement Learning & & & \\ \hline \multirow{4}{*}{Other-plane} & Without & \multirow{2}{*}{28.57\%} & \multirow{2}{*}{50.00\%} & \multirow{2}{*}{45.83\%} \\  & Reinforcement Learning & & & \\ \hline Other-plane & Original Model & 44.12\% & 57.69\% & 37.85\% \\ \hline \multirow{4}{*}{A330} & Without & \multirow{2}{*}{41.43\%} & \multirow{2}{*}{55.77\%} & \multirow{2}{*}{34.63\%} \\  & Reinforcement Learning & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Inference results of reinforcement learning ablation experiment.  
the other-plane category, the recall under this configuration reaches 59.40%, outperforming both the original model and single-parameter transfer. This indicates that it effectively balances recall and false positives. In contrast, transferring the entire classification and regression heads leads to performance degradation, suggesting that excessive transfer may introduce noise and compromise the model's generalization ability.