# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 4 Experimental Setup and Result
### Comparison with Other SOTA Methods  
All methods were evaluated on the DOTA-v1.5 datasets, a benchmark commonly used for oriented object detection in aerial imagery Figure 2. The dataset includes objects with varying scales and orientations, making it ideal for testing the robustness of different models. The evaluation metric used was the mAP, which measures the accuracy of object \begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Details** \\ \hline Hardware Platform & NVIDIA RTX 3090 \\ GPU & CUDA-enabled GPU \\ Memory & 24 GB \\ Framework & PyTorch \\ Optimizer & SGD with Momentum (0.9) \\ Learning Rate & 0.0025 (initial) \\ Batch Size & 16 \\ Training Iterations & 180,000 \\ Learning Rate Schedule & Decay at 120 k and 160 k iterations \\ Augmentations & Strong (Student Model), Weak (Teacher Model) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental Environment and Parameter Settings. detection across different classes. Both fully supervised and semi-supervised scenarios were considered in the comparisons. The results of the comparison are summarized in Table 2, where RASST is compared against the aforementioned methods under both fully supervised and semi-supervised settings. In Table 2 as presented RASST achieves the highest mAP of 67.70 in the fully supervised setting, outperforming all other methods. The integration of rotationally aware mechanisms and multi-scale feature fusion contributes significantly to this superior performance, as these components allow RASST to handle a wider variety of object orientations and sizes. In semi-supervised settings, where only 20% and 30% of the labeled data are used, RASST again outperforms the other methods, achieving mAPs of 59.23 and 62.10, respectively. This demonstrates RASST ability to effectively leverage unlabeled data through the PGL framework, which includes innovative loss functions such as the RAW Loss and GC Loss. While SOOD also uses a semi-supervised framework, RASST provides better results due to its advanced handling of rotational invariance and multi-scale features. Specifically, the ARC module and the MSFF module in RASST contribute to its enhanced performance. Although the Adaptive Rotated Convolution method shows strong performance due to its focus on rotational adjustments, RASST surpasses it by effectively combining these \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Fully Supervised mAP** & \
\begin{tabular}{c} **Semi-Supervised mAP** \\ **(20\% Labeled Data)** \\ \end{tabular} &
\begin{tabular}{c} **Semi-Supervised mAP** \\ **(30\% Labeled Data)** \\ \end{tabular} \\ \hline POE [22] & 65.12 & 56.78 & 59.34 \\ E-PVT [21] & 66.40 & 55.58 & 59.23 \\ OrientedR-CNN [18] & 67.10 & 57.20 & 60.15 \\ SOOD [19] & 66.50 & 56.90 & 59.80 \\ PSCD [20] & 66.38 & 53.93 & 57.86 \\ RoI-Trans.-ACM [17] & 67.02 & 57.85 & 59.99 \\ RASST (Ours) & 67.70 & 59.23 & 62.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of RASST framework with other SOTA methods on the DOTA-v1.5 dataset. Figure 2: Examples of RASST framework detection results. adjustments with a robust transformer-based architecture and semi-supervised learning strategies. RASST maintains its superior performance across different proportions of labeled data, indicating its scalability and robustness. The ability to maintain high accuracy even when the amount of labeled data is reduced is particularly beneficial for applications where labeled data is limited or expensive to obtain. In addition to quantitative comparisons, qualitative results were examined. RASST demonstrated superior ability in detecting small and densely packed objects that were challenging for other methods. The rotationally aware components of RASST provided better alignment with the ground truth orientations, reducing the number of false positives and improving overall detection quality. While RASST introduces additional computational complexity due to its dynamic convolutional operations and attention mechanisms, the performance gains justify the added complexity.