AV object detection faces fundamental challenges from dense grid representations
that waste computation and provide no robustness guarantees. We propose NTEFNet,
where detection operates over topological graphs derived via persistent homology
(H0 ⊕ H1), enabling the �rst formal stability bound for UAV detection: degradation
under perturbation ε is bounded by O(ε). A Hierarchical Graph Pyramid reduces
4K latency from 312ms to 84ms on Jetson Orin AGX. On VisDrone-VID, NTEF-Net
achieves +8.1% recall in low-light (p < 0.01), +9.3% MOTA under occlusion, and
12.4% robustness advantage over grid-based methods across 75 corruption conditions,
while reducing FLOPs by 35%. Rigorous ablation con�rms gains stem from topological
invariance, not graph structure alone.
AV object detection faces fundamental challenges from dense grid representations
that waste computation and provide no robustness guarantees. We propose NTEFNet,
where detection operates over topological graphs derived via persistent homology
(H0 ⊕ H1), enabling the �rst formal stability bound for UAV detection: degradation
under perturbation ε is bounded by O(ε). A Hierarchical Graph Pyramid reduces
4K latency from 312ms to 84ms on Jetson Orin AGX. On VisDrone-VID, NTEF-Net
achieves +8.1% recall in low-light (p < 0.01), +9.3% MOTA under occlusion, and
12.4% robustness advantage over grid-based methods across 75 corruption conditions,
while reducing FLOPs by 35%. Rigorous ablation con�rms gains stem from topological
invariance, not graph structure alone.