# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 4 Experimental Setup and Result
### Comparison with Other SOTA Methods
Furthermore, the architecture is optimized to ensure that the increase in computational demand does not hinder its applicability to real-time or large-scale tasks. The comparisons clearly indicate that RASST sets a new SOTA in oriented object detection, particularly in semi-supervised settings. The integration of advanced components such as POE [22], E-PVT [21], OrientedR-CNN [18], SOOD [19], PSCD [20], RoI-Trans.-ACM [17] framework allows RASST to excel in scenarios with challenging orientations and scales, making it a versatile and powerful tool for oriented object detection. To further corroborate the effectiveness of each component in the RASST framework, additional ablation studies were performed using the DOTA-v1.5 dataset. In these experiments, critical parts of the RASST framework were ablated in turn or selectively disabled to determine whether and how much each contributed to overall performance metrics. Specifically, an analysis was performed regarding Rotationally Aware Patch Embedding (RPE), the Adaptive Rotational Convolution (ARC) module, the Pseudo-Label Guided Learning (PGL) framework, and the Multi-Scale Feature Fusion (MSFF) module. The Mean Average Precision (mAP) values from each ablation configuration are summarized in Table 3. We see that by using 20% of labeled data, the complete RASST model has a fully supervised mAP of 67.70% and a semi-supervised mAP of 59.23%. Removing the RPE module decreases the fully supervised mAP to 65.85%, while also lowering the semi-supervised mAP to 57.10%, reflecting the importance of rotational robustness provided by the RPE module. Removing the ARC module has a similar negative effect, where the fully supervised mAP reduces to 66.02%. In comparison, the semi-supervised mAP lowers to 57.45%, thus reinforcing the necessity of dynamic orientation adaptation during feature extraction. Disabling the PGL framework yields a higher penalty, resulting in a 65.40% fully supervised mean Average Precision (mAP) score and a 53.82% semi-supervised mAP score. It reflects how critical pseudo-label refinement mechanisms, e.g., Rotation-Aware Adaptive Weighting (RAW) and Global Consistency (GC) losses, are to effective adaptation when using unlabeled data. Additionally, removing the MSFF module lowers the model ability to handle scale variations, further resulting in mAP scores of 66.10% in a fully supervised setting and 56.92% in a semi-supervised setting. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Variant** & **Fully Supervised mAP (\%)** & **Semi-Supervised mAP (20\% Labeled) (\%)** \\ \hline Full RASST Model & 67.70 & 59.23 \\ w/o Rotationally Aware Patch Embedding & 65.85 & 57.10 \\ w/o Adaptive Rotational Convolution & 66.02 & 57.45 \\ w/o Pseudo-Label Guided Learning & 65.40 & 53.82 \\ w/o Multi-Scale Feature Fusion & 66.10 & 56.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study of the RASST framework on the DOTA-v1.5 dataset. The results in Table 3 suggest that each component of the RASST approach contributes significantly to how effective it is, and the synergistic relation among these factors results in better recognition of focused objects regardless of changing scales and observation levels.