# A Solution to Product detection in Densely Packed Scenes

arXiv: https://arxiv.org/abs/2007.11946 · HTML: https://mlsanity.com/paper/2007.11946/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Unitail: Detecting, Reading, and Matching in Retail Scene](https://mlsanity.com/paper/2204.00298/) (2022)
- [Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detection](https://mlsanity.com/paper/2205.13060/) (2022)
- [Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout](https://mlsanity.com/paper/2207.05422/) (2022)
- [Adding New Categories in Object Detection Using Few-Shot Copy-Paste](https://mlsanity.com/paper/2206.05730/) (2022)

## Abstract (arXiv)

This work is a solution to densely packed scenes dataset SKU-110k. Our work is modified from Cascade R-CNN. To solve the problem, we proposed a random crop strategy to ensure both the sampling rate and input scale is relatively sufficient as a contrast to the regular random crop. And we adopted some of trick and optimized the hyper-parameters. To grasp the essential feature of the densely packed scenes, we analysis the stages of a detector and investigate the bottleneck which limits the performance. As a result, our method obtains 58.7 mAP on test set of SKU-110k.