CPD-FCOS: A Scale-Isolated P2 Pathway for UAV Small-Object Detection

Abstract

Small objects in UAV imagery fail for two coupled reasons. Repeated downsampling destroys their evidence—60.5% of VisDrone instances are COCO-small and the median object spans 26 pixels, so a typical target covers fewer than four cells at stride 8—and the in-box assignment rule then places most of their positive locations on object borders, where supervision is unreliable. The two reinforce each other: a finer level creates more candidate locations, but they help only if the assignment rule can select the reliable ones. We therefore design representation and supervision together in CPD-FCOS (Center-constrained P2 Detail-enhanced FCOS): a real stride-4 P2 level in a bidirectional pyramid, localized residual refinement at that level, and center-constrained, scale-bounded positive selection. Controlled experiments separate the two. On VisDrone, the stride-4 level supplies +2.98 mAP at 2.05× the end-to-end latency, while the refinement branch adds +0.55 mAP for 2.8% more parameters. Resolved by object size, the branch alone raises small-object recall but leaves AP unchanged; only constrained assignment converts that capacity into precision. The advantage is scale-specific, growing as objects shrink and reversing on the largest ones, and it is strongest in dense multi-class scenes—on vehicle-only and medium-object benchmarks, the pathway shifts the operating point rather than dominating. The result is a single-forward, reproducible reference design with an explicitly measured accuracy–efficiency trade-off.