Year: 2025
Type: article
Abstract: Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware Semi-Supervised Transformer framework designed to achieve high-precision detection under fully and semi-supervised conditions. RASST integrates a hybrid Vision Transformer architecture augmented with rotationally aware patch embeddings, adaptive rotational convolutions, and a multi-scale feature fusion (MSFF) module that employs cross-scale attention to enhance detection across object sizes. To address the scarcity of labeled data, we introduce a novel Pseudo-Label Guided Learning (PGL) framework, which refines pseudo-labels through Rotation-Aware Adaptive Weighting (RAW) and Global Consistency (GC) losses, thereby improving generalization and robustness against noisy supervision. Despite its lightweight design, RASST achieves superior performance on the DOTA-v1.5 benchmark, outperforming existing state-of-the-art methods in supervised and semi-supervised settings. The proposed framework demonstrates high scalability, precise orientation sensitivity, and effective utilization of unlabeled data, establishing a new benchmark for efficient oriented object detection in remote sensing imagery.
Source: Applied Sciences
DOI: 10.3390/app15095212
Institutions: Gachon University, Tashkent State University of Economics
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4410155650