# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 4 Results
### Dataset and Ablation Study
Notably, the score and F1 values of both decoder reconstruction (FBFFN-D) and full reconstruction (FBFFN-ALL) are significantly lower than those of encoder reconstruction, suggesting that decoder modifications may introduce redundant computations or interfere with feature representation, thereby degrading performance. Furthermore, the nearly identical parameter counts across all variants confirm that encoder reconstruction does not increase model complexity. Collectively, these findings indicate that targeted encoder reconfiguration effec \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Net** & **mAP\({}_{\text{50-95}}\)** & **mAP\({}_{\text{50}}\)** & **Recall** & **Score** & **F1** & **Params (M)** \\ \hline Dab & 0.302 & 0.825 & 0.506 & 0.658 & 0.865 & 47.05 \\ +CBAM & 0.305 & 0.813 & 0.501 & 0.710 & 0.887 & 47.05 \\ +CA & 0.320 & 0.839 & 0.522 & 0.714 & 0.896 & 47.06 \\ +SE & 0.311 & 0.838 & 0.515 & 0.745 & 0.889 & 47.05 \\ +ECA & 0.317 & 0.833 & 0.508 & 0.721 & 0.888 & 47.05 \\ +MDDCA & **0.326** & **0.854** & **0.523** & **0.746** & **0.899** & 47.28 \\ \hline \hline \end{tabular}  
* The + indicates that this part is added to the baseline model and the best part of the results is shown in bold.  
\end{table} 
Table 1: Performance comparison of channel attention mechanisms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Net** & **mAP\({}_{\text{50-95}}\)** & **mAP\({}_{\text{50}}\)** & **Recall** & **Score** & **F1** & **Params (M)** \\ \hline FBFFN-E & **0.321** & **0.843** & **0.511** & **0.740** & **0.906** & 47.06 \\ FBFFN-D & 0.310 & 0.833 & 0.512 & 0.715 & 0.889 & 47.06 \\ ALL & 0.317 & **0.843** & 0.506 & 0.705 & 0.901 & 47.06 \\ \hline \hline \end{tabular}  
* FBFFN-E: encoder reconstruction using FBFFN, FBFFN-D: decoder reconstruction using FBFFN, ALL: full reconstruction.  
\end{table} 
Table 2: Performance of FFN reconstruction strategies. tively enhances multi-scale feature representation without compromising model efficiency, achieving an optimal balance between accuracy and computational economy. This underscores the critical role of the encoder as the core feature extraction module in maintaining model performance. As shown in Table 3, each proposed module demonstrates distinct performance improvements when individually integrated into the Dab-Deformable architecture. Critically, the full combination of modules (ALL) achieves a state-of-the-art performance with the highest mAP\({}_{50-95}\) (0.338) and mAP\({}_{50}\) (0.867) while maintaining a competitive score (0.739) and F1 (0.903). Despite a marginal parameter increase, the synergistic integration of modules ensures comprehensive performance gains without significant computational overhead. Notably, while individual modules excel in specific metrics, the combined model effectively harmonizes these strengths, achieving balanced optimization across all tasks. The slight dip in recall (0.529) compared to Dab-TS (0.536) is offset by superior overall robustness, which is critical for real-world applications. These results conclusively demonstrate that our modules not only enhance specific capabilities when used independently but also exhibit complementary effects when combined, ultimately pushing the performance boundary of the deformable architecture.