# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 1 Introduction  
Oriented object detection in aerial imagery is a cornerstone of remote sensing, with applications spanning urban planning [1], disaster response [2], environmental monitoring [3], and security surveillance [4]. Unlike conventional object detection tasks, where objects are assumed to align with image axes, aerial imagery presents a unique challenge: objects often appear at arbitrary orientations. This variability in orientation increases the complexity of both data annotation and algorithmic detection, necessitating specialized approaches capable of effectively handling rotational invariance [5, 6, 7].  
Recent advancements in Vision Transformers (ViTs) have revolutionized computer vision, delivering superior performance across a wide range of tasks, including image classification and object detection [8, 9]. Concurrently, semi-supervised learning has emerged as a promising paradigm to address the scarcity of labeled data, a common challenge in real-world applications [10, 11]. However, despite their individual strengths, existing methods often fail to effectively integrate these advancements for oriented object detection.  
Specifically, they lack robust mechanisms to ensure rotational invariance [12] and are unable to fully leverage unlabeled data to improve generalization [13].  
In this paper, we present the RASST, a novel framework designed to address these challenges in oriented object detection. RASST leverages a hybrid Vision Transformer architecture that incorporates rotationally aware patch embeddings and adaptive rotational convolutions. To enhance the learning process in data-scarce environments, RASST employs a pseudo-label-guided learning framework that refines pseudo-labels through rotation-aware adaptive weighting (RAW) and global consistency (GC) losses. These innovations collectively enable the model to detect objects at arbitrary orientations with high precision, even under limited supervision.  
The key contributions of this work are as follows:  
* We propose a ViT-based backbone that integrates rotationally invariant patch embeddings and positional encodings, enhancing the model's ability to handle objects at varying orientations.
* A new convolutional module dynamically aligns convolutional kernels with object orientations, enabling the extraction of more robust features.
* By employing RAW and GC losses, our semi-supervised framework effectively utilizes unlabeled data, improving model generalization and pseudo-label reliability.
* The effectiveness of RASST is validated on the DOTA-v1.5 benchmark, where it achieves superior performance compared to existing methods in both fully supervised and semi-supervised settings.  
Through these contributions, RASST addresses critical gaps in the literature, providing a comprehensive solution to the challenges of oriented object detection in aerial imagery. By combining advancements in ViTs with innovative mechanisms for rotational invariance and semi-supervised learning, RASST sets a new benchmark for accuracy and robustness in this domain.  
The rest of the paper is organized as follows: Section 2 provides an overview of related work. Section 3 details the proposed methodology, including the architectural components and learning framework. Section 4 describes the experimental setup and compares RASST performance with SOTA methods. Section 5 discusses the implications and limitations of the results, and Section 6 provides concluding remarks and outlines future research directions.