Papers › Precise Detection in Densely Packed Scenes
Precise Detection in Densely Packed Scenes
Eran Goldman, Roei Herzig, Aviv Eisenschtat, Oria Ratzon, Itsik Levi, Jacob Goldberger, Tal Hassner
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}.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Dense Object Detection | SKU-110K | Soft-IoU + EM-Merger unit | AP | 49.2 | #4 of 5 | Archive leaderboard | report |
| Object Counting | CARPK | Soft-IoU + EM-Merger unit | MAE | 6.77 | #7 of 15 | Archive leaderboard | report |
| Object Counting | CARPK | Soft-IoU + EM-Merger unit | RMSE | 8.52 | #7 of 15 | Archive leaderboard | report |
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