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Rochester Institute of Technology
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Annotation refinement boosts YOLOv8's in-distribution mAP@50 by nearly 20 percentage points, while correcting class-ID conventions elevates out-of-distribution performance by about 25 points.
Human-in-the-loop bootstrapping can dramatically cut down the inspection effort needed for accurate landmine detection in UAV hyperspectral imaging.
