From Explanation to Correction: Training-Free Spatial Consistency Intervention for DETR-Based Small Object Detection

Small-object detection is fundamentally constrained by the limited and unstable spatial evidence available to a detector. A small object may occupy only a few visual tokens, causing its localization to vary substantially under semantics-preserving transformations or changes in spatial context. Existing methods mainly compensate for this instability through retraining, architectural modification, or black-box prediction aggregation, but rarely exploit the spatial inconsistency already exposed by a frozen detector. We propose Frozen Spatial Consistency Intervention (FSCI), a training- and fine-tuning-free framework that converts observable spatial violations into executable corrections. Counterfactual equivariant explanation identifies localization-sensitive instances through an invertible horizontal transformation and corrects predictions supported by cross-view consistency. Nearest-center ownership slicing recovers local visual detail while assigning each candidate to a unique spatial source, thereby reducing duplicate detections and candidate competition. We evaluate FSCI on five frozen DETR variants—DETR, Deformable DETR, Conditional DETR, DAB-DETR, and Anchor-DETR—across COCO, TinyPerson, and VisDrone2019, forming 15 detector–dataset settings. FSCI improves AP in all 15 settings. Further diagnostic experiments verify the directional and spatial faithfulness of the proposed explanations. These results demonstrate that spatial inconsistency is not merely an inference error, but an actionable source of supervision contained within a frozen detector.