SEDEIM: Small-Object Enhancement in General Object Detection

Detecting small objects in general object detection remains challenging in crowded scenes and near larger instances, where weak instance-specific cues can be obscured by dominant surrounding responses. To investigate this issue, we analyze DEIMv2 and find that missed small objects can still contain query-selected feature locations, indicating that spatial coverage alone is insufficient for successful detection. This motivates SEDEIM, a small-object-enhanced detector including the following three key components. Spatial-Enhanced Adapter supplements vision-transformer features with CNN details and spatially modulates their contribution. Context Decoupled Pyramid provides bidirectional cross-scale interaction while preserving a separate local-detail path to retain distinctions from neighboring instances. Local Evidence Refinement reuses features sampled by deformable attention to construct a localization-oriented representation for box prediction. Results on COCO val2017 show that SEDEIM-m achieves 54.2 AP and 35.5 APs, outperforming SOTA methods by 1.2 AP and 1.3 APs