YOLO-Barcode: towards universal real-time barcode detection on mobile devices

• Published in: Kompʹûternaâ optika Vol. 48; no. 4; pp. 592 - 600
• Main Authors: Ershova, D.M., Gayer, A.V., Bezmaternykh, P.V., Arlazarov, V.V.
• Format: Journal Article
• Language: English
• Published: Samara National Research University 01.08.2024
• Subjects: barcode dataset; barcode detection; convolutional neural networks; deep learning; object detection
• ISSN: 0134-2452; 2412-6179
• DOI: 10.18287/2412-6179-CO-1424

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.