SSM-YOLO11s: a lightweight and efficient model for small object detection in UAV aerial imagery

Abstract

Unmanned Aerial Vehicle (UAV) aerial photography is extensively utilized in security, traffic monitoring, and disaster rescue. However, UAV-captured images present significant challenges, including small target scales, dense distribution, and complex backgrounds. While conventional object detection algorithms like the YOLO series have made progress, they often struggle to balance accuracy and real-time performance in these resource-constrained environments. To address these issues, we propose SSM-YOLO11s, a lightweight model optimized for small object detection in aerial imagery. Our approach first introduces the Sitou module, which employs a deep-channel compression and shallow feature retention strategy with a secondary fusion branch to reduce parameters by 50% while enhancing fine-grained feature utilization. Furthermore, the lightweight SNGSConvE module is designed by integrating SNI, GSConvE, and CSPOmniKernel to mitigate feature misalignment and strengthen capture capabilities. Finally, a Multi-Scale Edge Enhancement (MSEE) module is constructed to fuse edge details across multiple scales, improving target discriminability. Experimental results on the VisDrone2019 dataset demonstrate that SSM-YOLO11s achieves a superior balance between precision and efficiency compared to state-of-the-art models.