# Precise Detection in Densely Packed Scenes

arXiv: https://arxiv.org/abs/1904.00853 · HTML: https://mlsanity.com/paper/1904.00853/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 30 of 63)

- [CounTR: Transformer-based Generalised Visual Counting](https://mlsanity.com/paper/2208.13721/) (2022)
- [Few-shot Object Counting with Similarity-Aware Feature Enhancement](https://mlsanity.com/paper/2201.08959/) (2022)
- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [AO2-DETR: Arbitrary-Oriented Object Detection Transformer](https://mlsanity.com/paper/2205.12785/) (2022)
- [Few-shot Object Counting and Detection](https://mlsanity.com/paper/2207.10988/) (2022)
- [Point, Segment and Count: A Generalized Framework for Object Counting](https://mlsanity.com/paper/2311.12386/) (2023)
- [H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection](https://mlsanity.com/paper/2210.06742/) (2022)
- [H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection](https://mlsanity.com/paper/2304.04403/) (2023)
- [LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding](https://mlsanity.com/paper/2605.27365/) (2026)
- [Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision](https://mlsanity.com/paper/2311.14758/) (2023)
- [Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark](https://mlsanity.com/paper/2207.11169/) (2022)
- [Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art](https://mlsanity.com/paper/2309.04902/) (2023)
- [Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales](https://mlsanity.com/paper/2103.11781/) (2021)
- [Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances](https://mlsanity.com/paper/2502.04268/) (2025)
- [A Fixed-Point Approach to Unified Prompt-Based Counting](https://mlsanity.com/paper/2403.10236/) (2024)
- [CoHD: A Counting-Aware Hierarchical Decoding Framework for Generalized Referring Expression Segmentation](https://mlsanity.com/paper/2405.15658/) (2024)
- [Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation](https://mlsanity.com/paper/2206.10075/) (2022)
- [Vision as Unified Multimodal Generation](https://mlsanity.com/paper/2607.06560/) (2026)
- [StandardSim: A Synthetic Dataset For Retail Environments](https://mlsanity.com/paper/2202.02418/) (2022)
- [YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems](https://mlsanity.com/paper/2307.13901/) (2023)
- [YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection](https://mlsanity.com/paper/2512.23273/) (2025)
- [Counting and Locating High-Density Objects Using Convolutional Neural Network](https://mlsanity.com/paper/2102.04366/) (2021)
- [Decoupled IoU Regression for Object Detection](https://mlsanity.com/paper/2202.00866/) (2022)
- [ShelfHelp: Empowering Humans to Perform Vision-Independent Manipulation Tasks with a Socially Assistive Robotic Cane](https://mlsanity.com/paper/2405.20501/) (2024)
- [Dense Center-Direction Regression for Object Counting and Localization with Point Supervision](https://mlsanity.com/paper/2408.14457/) (2024)
- [Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning](https://mlsanity.com/paper/2412.11582/) (2024)
- [TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection](https://mlsanity.com/paper/2104.11435/) (2021)
- [Unitail: Detecting, Reading, and Matching in Retail Scene](https://mlsanity.com/paper/2204.00298/) (2022)
- [Retail-786k: a Large-Scale Dataset for Visual Entity Matching](https://mlsanity.com/paper/2309.17164/) (2023)
- [A Benchmark Grocery Dataset of Realworld Point Clouds From Single View](https://mlsanity.com/paper/2402.07819/) (2024)

## Abstract (arXiv)

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 urlwww.github.com/eg4000/SKU110KCVPR19.