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Unitail: Detecting, Reading, and Matching in Retail Scene

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arxiv 2204.00298 v4 pith:5LQ674WE submitted 2022-04-01 cs.CV

Fangyi Chen , Han Zhang , Zaiwang Li , Jiachen Dou , Shentong Mo , Hao Chen , Yongxin Zhang , Uzair Ahmed
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Chenchen Zhu Marios Savvides
This is my paper · ORCID
classification cs.CV
keywords matchingproductreadingretailunitaildatasetdatasetsdetecting
verification ladder T0 review T1 audit T2 compute T3 formal
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To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped instances annotated, the Unitail offers a detection dataset to align product appearance better. Furthermore, it provides a gallery-style OCR dataset containing 1454 product categories, 30k text regions, and 21k transcriptions to enable robust reading on products and motivate enhanced product matching. Besides benchmarking the datasets using various state-of-the-arts, we customize a new detector for product detection and provide a simple OCR-based matching solution that verifies its effectiveness.

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  1. Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction

    cs.CV 2024-11 conditional novelty 4.0 of 10

    The paper presents a GS1-based, studio-photographed food product image dataset of 1,034 images and 30 object detection labels, with baseline YOLOv5 and ResNet50 results.

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