Precise Detection in Densely Packed Scenes
Eran Goldman, Roei Herzig, Aviv Eisenschtat, Oria Ratzon, Itsik Levi, Jacob Goldberger, Tal Hassner
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/eg4000/SKU110K_CVPR19OfficialIn papertf★ 0
- github.com/Media-Smart/SKU110K-DenseDetpytorch★ 111
- github.com/tyomj/product_detectionpytorch★ 49
- github.com/skrish13/SKU110K-benchmarknone★ 0
- github.com/skrish13/SKU110K-evaluationnone★ 0
Abstract
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 www.github.com/eg4000/SKU110K_CVPR19.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| CARPK | Soft-IoU + EM-Merger unit | MAE | 6.77 | — | Unverified |