  Man-made scenes can be densely packed, containing numerous objects, often
identical, positioned in close proximity. We show that precise object detection
in such scenes remains a challenging frontier even for state-of-the-art object
detectors. We propose a novel, deep-learning based method for precise object
detection, designed for such challenging settings. Our contributions include:
(1) A layer for estimating the Jaccard index as a detection quality score; (2)
a novel EM merging unit, which uses our quality scores to resolve detection
overlap ambiguities; finally, (3) an extensive, annotated data set, SKU-110K,
representing packed retail environments, released for training and testing
under such extreme settings. Detection tests on SKU-110K and counting tests on
the CARPK and PUCPR+ show our method to outperform existing state-of-the-art
with substantial margins. The code and data will be made available on
\url{www.github.com/eg4000/SKU110K_CVPR19}.
