A Normalized Gaussian Wasserstein Distance for Tiny Object Detection

Tiny Object Detection: New Normalized Wasserstein Distance Finds Small Things Better

Finding tiny things in pictures is hard because they take up only a few pixels and can disappear when methods expect bigger shapes.
Scientists changed how we compare a guess and the real box.
Instead of using overlap, they treat boxes like soft, blurry spots and compare them as 2D Gaussian shapes.
This new score, called Normalized Wasserstein Distance (NWD), is less sensitive to tiny shifts so detectors catch the object even if it moves a bit, it also reduce false drops.
When added into modern detectors it helps them learn and pick better boxes, especially for tiny objects.
Tests on a special set of pictures made for small targets, the AI-TOD dataset, show the idea works much better than older ways.
The change is simple to add and does not need big extra work, so phones and cameras could get better at spotting small things like drones, birds, or far cars.
That makes vision systems more reliable where small detail matters, and thats good news for many real uses.

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A Normalized Gaussian Wasserstein Distance for Tiny Object Detection

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