SOD-MSC: Multi-Scale Context-Focused Detection for Small Traffic Objects in Aerial Images

Abstract

The detection of small objects in aerial traffic scenes is vital for advancing intelligent transportation systems (ITS), yet traditional methods often suffer from scale variation, background complexity, and insufficient context utilization. To address these challenges, we propose SOD-MSC, a YOLOv8-based small object detector designed for remote sensing images. SOD-MSC integrates a global multi-scale (GMS) module to enhance global perception and multi-scale fusion, a lightweight fusion feature pyramid network (LFFPN) to strengthen spatial correlations and suppress background noise, and an adaptive RT-DETR-based detection head with Inner_SIoU loss to improve localization accuracy and convergence. Experiments on VisDrone2019-DET and HazyDet demonstrate that SOD-MSC achieves 39.9% and 49% on mAP@0.5, with only 5.22 M parameters and 11.2 GFLOPs, surpassing several baselines and state-of-the-art models in balancing accuracy and efficiency. These results highlight the effectiveness of SOD-MSC for dense small object detection in complex traffic environments, with promising applications in congestion monitoring, vehicle counting, and related ITS tasks.