  While deep learning-based general object detection has made significant
strides in recent years, the effectiveness and efficiency of small object
detection remain unsatisfactory. This is primarily attributed not only to the
limited characteristics of such small targets but also to the high density and
mutual overlap among these targets. The existing transformer-based small object
detectors do not leverage the gap between accuracy and inference speed. To
address challenges, we propose methods enhancing sampling within an end-to-end
framework. Sample Points Refinement (SPR) constrains localization and
attention, preserving meaningful interactions in the region of interest and
filtering out misleading information. Scale-aligned Target (ST) integrates
scale information into target confidence, improving classification for small
object detection. A task-decoupled Sample Reweighting (SR) mechanism guides
attention toward challenging positive examples, utilizing a weight generator
module to assess the difficulty and adjust classification loss based on decoder
layer outcomes. Comprehensive experiments across various benchmarks reveal that
our proposed detector excels in detecting small objects. Our model demonstrates
a significant enhancement, achieving a 2.9\% increase in average precision (AP)
over the state-of-the-art (SOTA) on the VisDrone dataset and a 1.7\%
improvement on the SODA-D dataset.
