S-YOLO: An enhanced small object detection method based on adaptive gating strategy and dynamic multi-scale focus module.

Abstract

Detecting small objects in drone aerial imagery presents significant challenges, particularly when algorithms must operate in real-time under computational constraints. To address this issue, we propose S-YOLO, an efficient and streamlined small object detection framework based on YOLOv10. The S-YOLO architecture emphasizes three key innovations: (1) Enhanced Small Object Detection Layers: These layers augment semantic richness to improve detection of diminutive targets. (2) C2fGCU Module: Incorporating Gated Convolutional Units (GCU), this module adaptively modulates activation strength through deep feature analysis, enabling the model to concentrate on salient information while effectively mitigating background interference. (3) Dynamic Multi-Scale Fusion (DMSF) Module: By integrating SE-Norm with multi-scale feature extraction, this component dynamically recalibrates feature weights to optimize cross-scale information integration and focus. S-YOLO surpasses YOLOv10-n, achieving mAP50:95 improvements of 5.3%, 4.4%, and 1.4% on the VisDrone2019, AI-TOD, and DOTA1.0 datasets, respectively. Notably, S-YOLO maintains fewer parameters than YOLOv10-n while processing 285 images per second, establishing it as a highly efficient solution for real-time small object detection in aerial imagery.