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YOLO-Barcode: towards universal real-time barcode detection on mobile devices
D.M. Ershova 1,2, A.V. Gayer 1,3, P.V. Bezmaternykh 1,3, V.V. Arlazarov 1,3

1 Smart Engines Service LLC, 117312, Russia, Moscow, Prospect 60-Letiya Oktyabrya 9;
2 Moscow Institute of Physics and Technology,
141701, Russia, Moscow Region, Dolgoprudny, Institutskiy per. 9;
3 Federal Research Center “Computer Science and Control” of RAS,
119333, Moscow, Russia, Vavilova str. 44, corp. 2

 PDF, 4042 kB

DOI: 10.18287/2412-6179-CO-1424

Pages: 592-600.

Full text of article: English language.

Abstract:
Existing approaches to barcode detection have a number of disadvantages, including being tied to specific types of barcodes, computational complexity or low detection accuracy. In this paper, we propose YOLO-Barcode – a deep learning model inspired by the You Only Look Once approach that allows to achieve high detection accuracy with real-time performance on mobile devices. The proposed model copes well with a large number of densely spaced barcodes, as well as highly elongated one-dimensional barcodes. YOLO-Barcode not only successfully detects the huge variety of barcode types, but also classifies them. Comparing with the previous universal barcode detector DilatedModel based on semantic segmentation, the YOLO-Barcode is 4 times faster and achieves state-of-the-art accuracy on the ZVZ-real public dataset: 98.6% versus 88.9% by F1-score. The analysis of existing publicly available datasets reveals the absence of many real-life scenarios of mobile barcode reading. To fill this gap, the new “SE-barcode” dataset is presented. The proposed model, used as a baseline, achieves a 92.11% by F1-score on this dataset.

Keywords:
barcode detection, barcode dataset, object detection, convolutional neural networks, deep learning.

Citation:
Ershova DM, Gayer AV, Bezmaternykh PV, Arlazarov VV. YOLO-Barcode: towards universal real-time barcode detection on mobile devices. Computer Optics 2024; 48(4): 592-600. DOI: 10.18287/2412-6179-CO-1424.

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