Field notes on CUAS, defense AI, 2POV drone-to-drone capture, and the operational realities of building models that perform against real adversary drones — not the ones in stock footage.
The adversary drone market is commoditizing faster than our defenses. A look at what training data the next generation of C-UAS needs.
Read →A dedicated camera operator tracking the target drone changes what a dataset can teach a model. Here's why drone-to-drone 2POV matters.
Read →Your detector is only as good as the distribution it was trained on. Most public datasets were built for a world that doesn't shoot back.
Read →Detectors don't fail on the average case. They fail on the weird one. Engineering a dataset that covers the long tail on purpose.
Read →Offshore annotation is cheap. Offshore capture is a non-starter for most defense and CUAS programs. Why the crew matters as much as the payload.
Read →Getting a detector trained is the easy part. Getting it fielded, monitored, and re-trained on program timelines is where most efforts die.
Read →Automatic Target Recognition lives and dies on class definitions. Get the ontology wrong and no amount of data will save the model.
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