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A dataset for illuminant- and device- invariant colour barcode decoding with cameras.

Michela Lecca¹, Paola Lecca²

¹Fondazione Bruno Kessler, Digital Industry Center, Technologies of Vision, via Sommarive 18, Trento 38123, Italy.

|January 18, 2024

Summary

Researchers developed COCO-10, a public dataset for color barcode decoding. This dataset addresses challenges in color barcode recognition across various conditions, aiding AI development.

Keywords:

Colour dependence on light, Device and material Colour images Colour marker detection and decoding

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Area of Science:

Computer Vision
Data Science
Image Processing

Background:

Traditional barcodes use black and white lines for data encoding.
Color barcodes offer increased data capacity but face decoding challenges due to variations in lighting, printing, and display devices.
Existing research lacks public datasets with experimental data for training and testing color barcode decoding algorithms.

Purpose of the Study:

To introduce COCO-10, a comprehensive public dataset for color barcode images.
To facilitate the development and evaluation of robust color barcode decoding algorithms.
To address the need for standardized benchmarks in color barcode research.

Main Methods:

The COCO-10 dataset comprises 5,400 images of 150 unique color barcodes, printed on different paper types and captured under varied lighting conditions using multiple smartphone cameras.
Color barcodes were created by assigning random colors from a 10-color palette to black and white barcode patterns.
An additional 300 synthetic images with cluttered backgrounds were generated to simulate real-world scenarios, along with corresponding masks for barcode localization.

Main Results:

The COCO-10 dataset contains a total of 11,700 images, including real-world and synthetic samples, with corresponding masks.
The dataset accounts for color variability arising from different light sources, printer/camera gamuts, and paper qualities.
It provides a benchmark for testing barcode detection and decoding algorithms under diverse environmental conditions.

Conclusions:

COCO-10 serves as a valuable resource for advancing color barcode technology and developing more accurate decoding algorithms.
The dataset can also be utilized for research in machine color constancy, gamut mapping, and color correction.
This public dataset aims to accelerate progress in the field by providing a standardized platform for research and development.