# Spatial Shape-Aware Network for Elongated Target Detection
## 7 Results and Discussion  
### Ablation Study Analysis  
(1) Ablation Study on Module Effectiveness  
Ablation studies were carried out on the HRSC2016 and UCAS-AOD datasets to assess the impact of different modules on performance. The results on the HRSC2016 dataset are presented in Table 2. An mAP of 85.1% was achieved by the baseline model, primarily because standard CNN convolution structures struggle to effectively model and extract features for elongated targets with multiple rotational directions. After incorporating BCSF, the detector's performance improved by 3.4%. This demonstrates that, in rotation detectors, guiding the convolution kernel at each position within the receptive field based on edge location features enables free deformation and displacement. The targets' shape and directional information is better captured by the network due to this, thereby enhancing the detector's performance. After incorporating SSM, the detector's performance further improved by 2.1%. This is because the module introduces shape-related parameters into the sample selection process, enabling a more comprehensive representation of elongated targets during threshold-based discrimination. This improvement benefits subsequent regression and classification tasks. Adding PEM resulted in an additional performance gain of 1.8%. By incorporating positional information of each sample in the loss function, the model prioritizes high-potential positive samples during the training process, thereby further boosting detector performance. When both SSM and PEM were used simultaneously, the detector achieved a performance increase of 4.1%. When both BCSF and SSM were used simultaneously, the detector achieved a performance increase of 4.7%. Finally, when all three modules (BCSF, SSM, and PEM) were used together, the detector's performance improved by 6.1%, showcasing the complementary nature of these modules. As shown in Table 3, similar experimental outcomes were observed on the UCAS-AOD dataset. The integration of different modules demonstrated superior performance compared to using a single module. BCSF and SSM collectively enhanced feature extraction by adapting to target shapes while enabling high-potential sample selection, resulting in better classification and regression outcomes. Additionally, the experiments confirmed that the proposed modules do not conflict with one another. When all modules were used together, the model achieved an mAP of 91.3%, demonstrating the effectiveness of the proposed network. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**With BGSF** & **With SSM** & **With PEM** & **mAP (\%)** \\ \hline \(\times\) & \(\times\) & \(\times\) & 85.1 \\ \(\times\) & \(\checkmark\) & \(\times\) & 87.2 \\ \(\times\) & \(\times\) & \(\checkmark\) & 86.9 \\ \(\checkmark\) & \(\times\) & \(\times\) & 88.5 \\ \(\checkmark\) & \(\checkmark\) & \(\times\) & 89.8 \\ \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & 89.2 \\ \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & 91.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effects of each component of SSAD-NET on HRSC2016 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**With BGSF** & **With SSM** & **With PEM** & **mAP (\%)** \\ \hline \(\times\) & \(\times\) & \(\times\) & 85.7 \\ \(\times\) & \(\checkmark\) & \(\times\) & 87.4 \\ \(\times\) & \(\times\) & \(\checkmark\) & 87.1 \\ \(\checkmark\) & \(\times\) & \(\times\) & 88.7 \\ \(\checkmark\) & \(\checkmark\) & \(\times\) & 90.1 \\ \(\times\) & \(\checkmark\) & \(\checkmark\) & 89.4 \\ \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & 91.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effects of each component of SSAD-Net on UCAS-AOD dataset. In SSM, \(w\) is set to 4 by default, and is used to adjust the gradual reduction of the weighting parameter as the aspect ratio of objects increases. In datasets featuring predominantly high-aspect-ratio objects, increased values of \(w\) typically enhance performance.