CASA-Net: Context-Aware Small-Object Adaptation Network for UAV Aerial Images

Abstract

Detecting small targets in UAV aerial imagery is inherently difficult because these objects occupy only a small number of pixels and are highly susceptible to cluttered backgrounds, dense spatial arrangements, and pronounced scale variation. To address this problem, we propose CASA-Net (Context-Aware Small-object Adaptation Network), a context-aware detector built on a YOLOv26s baseline with three coordinated improvements: an Enhanced Small-Target-Aware Label Assignment mechanism for stronger supervision of tiny instances, a Multi-scale Feature Enhancement Module for richer contextual representation and spatial discrimination, and an aerial-specific augmentation pipeline for improved robustness to viewpoint, scale, and motion blur. Experiments on the VisDrone and RSOD benchmarks demonstrate that CASA-Net consistently outperforms the baseline and competing methods. On VisDrone, it achieves 47.0% mAP0.5 and 25.2% small-object mAP0.5, while on RSOD it reaches 78.3% mAP0.5. In addition, the model achieves real-time inference speeds above 100 FPS on both datasets using an RTX 3090, with 13.9 M parameters and 26.2 GFLOPs. Taken together, these findings show that CASA-Net is an accurate and efficient framework for UAV small-object detection through the joint improvement of supervision, feature representation, and data adaptation.