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The Bird Tax: Classification Quality and the Real Cost of Counter-Drone Sensing

We assess that the counter-drone detection contest has moved from detection to discrimination. Finding small, slow, low objects is a solved problem at every price point; deciding whether the object is a drone, a bird, or weather is not, and the error rates carry real costs — operator workload, alarm fatigue, wasted engagements, and rules-of-engagement friction on one side, catastrophic misses on the other. Because the penalty for a false negative dwarfs the penalty for a false alarm, operators set sensitive thresholds and live with the noise. Classification quality, sustained through retrained models and multi-sensor fusion, is therefore becoming the parameter on which procurements are actually decided.