# Spatial Shape-Aware Network for Elongated Target Detection
## 7 Results and Discussion
### Comparative Experiments  
(1) Results on the UCAS-AOD Dataset  
Our proposed SSAD-Net was compared with several methods on the UCAS-AOD dataset. As shown in Table 6, our approach achieved outstanding results, particularly for the ship and airplane categories, with APs of 89.58% and 94.13%, respectively. Furthermore, we achieved the best overall mAP of 91.34% across all categories. Partial visual detection outcomes for ground targets from the UCAS-AOD dataset are shown in Figure 3. As illustrated, our network can accurately detect densely arranged targets with multiple orientations. For arbitrarily oriented targets, the anchor boxes adaptively align with the spatial orientation of these targets to ensure precise rotation compatibility. As depicted in the figure, our detector effectively identifies densely packed airplanes of varying sizes and orientations, even in complex scenes with highly intricate spatial arrangements. Additionally, our network demonstrates robust detection performance in urban road environments with dense, small-scale vehicles. These results highlight the ability of our detector to handle densely packed targets across diverse arrangements, indicating strong generalization capabilities. To further explore performance variations, we used box plots to display the distribution of mAP values across different methods. These box plots, as shown in Figures 4 and 5, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(w\) **= 0.3** & \(w\) **= 1** & \(w\) **= 4** & \(w\) **= 5** & \(w\) **= 7** \\ \hline mAP (\%) & 89.4 & 90.1 & 91.2 & 90.6 & 90.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Impact of parameter \(w\) on mAP performance on the UCAS-AOD dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(w\) **= 0.3** & \(w\) **= 1** & \(w\) **= 4** & \(w\) **= 5** & \(w\) **= 7** \\ \hline mAP (\%) & 89.5 & 90.2 & 91.3 & 90.8 & 90.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Impact of parameter \(w\) on mAP performance on the HRSC2016 dataset. clearly illustrate the distribution and central tendency of the results. The box plots demonstrate that, compared to the baseline model, SSAD-Net consistently achieves the highest performance in most cases. The relatively narrow interquartile range indicates that the model exhibits stable performance across multiple trials. For the UCAS-AOD dataset, SSAD-Net achieved an mAP of 91.34%, with a 95% confidence interval of [91.17%, 92.50%], outperforming other methods. This demonstrates that it significantly exceeds the performance of the second-best method, S2 ANet, which has an mAP of 89.99% and a confidence interval of [89.78%, 90.20%]. (2) Results on the HRSC2016 Dataset  
Our proposed SSAD-Net was compared with several state-of-the-art methods on the HRSC2016 dataset. Our network achieved the best overall mAP of 91.2% across all categories, as shown in Table 7.  
Partial visual detection results for ground targets from the HRSC2016 dataset are shown in Figure 6. It can be observed that our network accurately detects elongated targets. For arbitrarily oriented objects, the anchor boxes precisely align with their spatial orientations, achieving rotational adaptability. Even in scenarios where multiple elongated ships are densely packed together, our detector accurately identifies these targets under various spatial arrangements. Additionally, for ships with different aspect ratios but belonging to the same class, our detector consistently classifies them into the same category. These results highlight the effectiveness of our network in facilitating the precise detection of elongated targets. BGSF enhances the network's ability to respond to feature variations across different objects in the image, thereby improving feature extraction for elongated, variably scaled, and multi-directionally rotated targets. SSM adjusts the sample selection strategy based on object shape characteristics, designing reasonable thresholds that prevent high-potential samples from being prematurely discarded.