A Geometry-Conditioned Symmetry-Aware Domain-Robust Observation Correction Front-End for Anti-UAV Visual Perception

Although the bounding boxes produced by an object detector provide real-time target localization cues for anti-UAV visual perception, they remain susceptible to geometric deviations under long-range small-target conditions, complex backgrounds, motion blur, and cross-domain environmental variations. Consequently, these detector outputs cannot always serve directly as stable observations for state estimation, trajectory prediction, and interception control. To address this issue, this paper proposes CDBR-Net, a causally conditioned domain-robust observation correction network for post-detection UAV bounding-box refinement. CDBR-Net employs a shared encoder, disentangled multi-branch representations, and a quality-aware gating mechanism to jointly produce a corrected observation box, a robust representation, and an observation uncertainty estimate. To preserve this conditional environment-transformation symmetry without suppressing geometry-induced symmetry breaking, CDBR-Net constructs geometry-conditioned cross-domain sample pairs and imposes cross-domain consistency and geometry-sensitivity preservation constraints. After training on 8749 post-detection observations, CDBR-Net is evaluated on 997 aligned validation observations from three simulated scene domains. It reduces the YOLO bounding-box mean absolute error (MAE) by 6.8%, from 0.002924 to 0.002725, and increases the intersection over union (IoU) by 0.007979, from 0.852167 to 0.860146. It further reduces MAE by 1.7% and increases IoU by 0.002031 relative to the YOLO + MLP Residual baseline. Ablation studies demonstrate the complementary roles of geometry-conditioned cross-domain consistency and geometry-sensitivity preservation. These results indicate that CDBR-Net provides a more stable and geometrically consistent post-detection observation interface for anti-UAV visual perception.