# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 5 Discussion  
The experimental results demonstrate the effectiveness of the RASST in improving oriented object detection in aerial imagery. Its SOTA performance in fully supervised and semi-supervised settings underscores its robustness and scalability. By incorporating rotationally aware patch embeddings and adaptive rotational convolutions, RASST addresses the critical challenge of detecting objects at arbitrary orientations, making it especially valuable for applications such as disaster response and urban planning, where precise orientation is essential.  
RASST success in semi-supervised settings highlights the impact of its PGL framework. Through the refinement of pseudo-labels using RAW and GC losses, the model effectively utilizes unlabeled data, extending its applicability to scenarios where labeled data is scarce or costly to obtain. The MSFF module enhances RASST versatility by improving detection of objects at varying scales. The adaptive cross-scale attention mechanism ensures accurate detection of both small, densely packed objects and larger, more sparse ones, making the model adaptable to diverse datasets and use cases. Despite its strengths, RASST has limitations. The rotational and semi-supervised mechanisms introduce computational overhead, which may hinder performance in resource-constrained environments or on ultra-large datasets. Additionally, reliance on a fixed confidence threshold for pseudo-labeling could exclude useful low-confidence data. Optimizing these aspects could further enhance the model's scalability and efficiency. The DOTA-v1.5 dataset used for evaluation, while comprehensive, does not cover extreme conditions such as severe occlusions, adverse weather, or highly dynamic scenes. Testing on additional datasets with such conditions could validate the model robustness further. Similarly, extending evaluations to domain-specific datasets, like those for maritime environments or autonomous navigation, could broaden RASST applicability. The promising results achieved by RASST open avenues for future research. Exploring more efficient architectures or optimization techniques could reduce computational costs, enhancing its deployment on edge devices. Incorporating uncertainty modeling into the pseudo-labeling framework could improve the handling of noisy or low-confidence labels, further strengthening its performance in semi-supervised scenarios. Integrating RASST with active learning or few-shot learning approaches could enable effective operation with even smaller labeled datasets, addressing annotation challenges more sustainably. In future work, we plan to explore model optimization techniques such as structured pruning and post-training quantization to mitigate the computational overhead of the dynamic elements of RASST. Model pruning allows for the removal of redundant parameters and layers, while quantization reduces numerical precision during computation, thus leading to substantial savings in both memory footprint and computational requirements. By incorporating these efficient optimization techniques, our goal is to maintain RASST's high detection accuracy while also enhancing its run-time efficiency, making it a competitive candidate for deployment in edge computing and resource-limited settings. Such advances would make the framework more suitable for real-world applications in environments where data acquisition is limited or computational resources are inadequate.  
RASST advances oriented object detection by integrating robust rotationally aware mechanisms, effective semi-supervised learning strategies, and scalable multi-scale feature fusion. While there is room for improvement, this framework provides a strong foundation for future exploration, offering a pathway to more efficient, accurate, and generalizable solutions for aerial imagery tasks. These contributions hold significant potential to inspire the next generation of object detection models in both research and practical applications.