Technical Reflection
Why Temporal Context Matters
Small airborne targets are often near the visual noise floor. A single frame may not contain enough evidence for stable detection, especially under camera motion, blur, and cluttered backgrounds.
Temporal modeling helps aggregate weak cues across frames. Instead of asking "is this target obvious now?" the model asks "is there persistent evidence of a target over time?" This shift improves continuity and reduces fragile frame-to-frame decisions.
STARD-Net, V-USDT, and KRAfT all follow this principle in different ways: sequence-aware feature extraction, temporal association, and geometry-aware recovery under missing observations.