A U-Net with optimized anti-aliased masks and multi-labeler training achieves 97.7% recall and 3.7%-diameter accuracy in granular particle localization, with human-labeler bias defining the accuracy floor.
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U-Net based particle localization in granular experiments: Accuracy limits and optimization
A U-Net with optimized anti-aliased masks and multi-labeler training achieves 97.7% recall and 3.7%-diameter accuracy in granular particle localization, with human-labeler bias defining the accuracy floor.