QAD-D-FINE: QUERY-ADAPTIVE SAMPLING AND REDUNDANCY SUPPRESSION FOR DENSE SMALL OBJECT DETECTION

Dense small object detection is challenging because weak fine-grained cues and crowded layouts jointly cause missed objects and redundant responses. We propose QAD-D-FINE, a query-adaptive, redundancy-suppression extension of D-FINE for dense small-object scenes. QAD-D-FINE contains two complementary components. Query-Adaptive Sampling (QAS) augments the decoder with a high-resolution P2 feature and continuously adjusts deformable sampling offsets according to the incoming query scale and confidence, allowing confident small-object queries to aggregate more local evidence while preserving context for uncertain queries. Matching-based Redundant Query Suppression (MRS) uses the standard one-to-one matching result as an anchor, assigns unmatched queries to same-class ground-truth instances with geometry-normalized ownership, and jointly suppresses redundant classification and localization responses during training. On VisDrone, QAD-D-FINE reaches 33.1 AP and 23.6 APsmall, improving D-FINE by 1.7 and 3.3 points, respectively, while retaining 166.7 FPS with 10.1M parameters at 640 × 640 input. Experiments on SODA-D and UAVDT further demonstrate consistent gains in dense small-object scenarios.