A multi-scale neural architecture enhanced with attention and super-resolution for aerial small object detection

Detecting small objects reliably is particularly difficult in modern neural architectures, where scale imbalance, background clutter, and high object density frequently degrade feature quality and prediction accuracy. To address these challenges, we propose YOLO-Super Resolution and Attention (YOLO-SRA), a multi-scale neural architecture enhanced with attention and super-resolution. The architecture introduces High-Resolution Feature Enhancement (HRFE) to better represent small objects without incurring high computational cost, a Grouped Multi-Scale Split Attention (GMSA) mechanism to efficiently extract features from densely distributed objects, and Weighted Fine-Grained Cross-Scale Fusion (WFCF) network for adaptive multi-scale feature integration with Unmanned Aerial Vehicle (UAV)-specific adjustments. The Spatial-Attentive Non-Maximum Suppression (SA-NMS) strategy is further employed to reduce missed detections in overlapping regions. Extensive experiments on the VisDrone dataset demonstrate that YOLO-SRA outperforms the baseline YOLOv11, achieving 12.5% and 11.3% increase in mAP 50 and mAP <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>50</mml:mn> <mml:mo>:</mml:mo> <mml:mn>95</mml:mn> </mml:mrow> </mml:msub> </mml:math> , respectively, while reducing the parameter count by 6.0M. These results highlight the effectiveness of the proposed approach as a methodological contribution to neural systems for small object detection in aerial image.