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1D Barcode Detection via Integrated Deep-Learning and Geometric Approach

Applied sciences, 2019-08, Vol.9 (16), p.3268 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app9163268

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  • Title:
    1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
  • Author: Xiao, Yunzhe ; Ming, Zhong
  • Subjects: 1D barcode ; Accuracy ; Algorithms ; Automation ; Bar codes ; barcode detection ; Benchmarks ; Computer applications ; Deep learning ; Experimentation ; Image resolution ; Image segmentation ; Integrated approach ; line segment detection ; Localization ; Neural networks ; object detection ; Segmentation ; Sensors
  • Is Part Of: Applied sciences, 2019-08, Vol.9 (16), p.3268
  • Description: Vision-based 1D barcode reading has been the subject of extensive research in recent years due to the high demand for automation in various industrial settings. With the aim of detecting the image region of 1D barcodes, existing approaches are both slow and imprecise. Deep-learning-based methods can locate the 1D barcode region fast but lack an adequate and accurate segmentation process; while simple geometric-based techniques perform weakly in terms of localization and take unnecessary computational cost when processing high-resolution images. We propose integrating the deep-learning and geometric approaches with the objective of tackling robust barcode localization in the presence of complicated backgrounds and accurately detecting the barcode within the localized region. Our integrated real-time solution combines the advantages of the two methods. Furthermore, there is no need to manually tune parameters in our approach. Through extensive experimentation on standard benchmarks, we show that our integrated approach outperforms the state-of-the-art methods by at least 5%.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3417
    EISSN: 2076-3417
    DOI: 10.3390/app9163268
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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