A Solution to Product detection in Densely Packed Scenes
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2020.
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Citing papers per year: 2022: 4.
4 papers.
- 2022Unitail: Detecting, Reading, and Matching in Retail Scenecited by 2
- 2022Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detectioncited by 1
- 2022Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkoutcited by 1
- 2022Adding New Categories in Object Detection Using Few-Shot Copy-Pastecited by 0
- 2022Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout12 Jul 2022
- 2022Adding New Categories in Object Detection Using Few-Shot Copy-Paste12 Jun 2022
- 2022Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detection25 May 2022
- 2022Unitail: Detecting, Reading, and Matching in Retail Scene1 Apr 2022