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Effective Barcode Hunter via Semantic Segmentation in the Wild

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11961))

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Abstract

Barcodes are popularly used for product identification in many scenarios. However, locating them on product images is challenging. Half-occlusion, distortion, darkness or targets being too small to recognize can often add to the difficulties using conventional methods. In this paper, we introduce a large-scale diverse barcode dataset and adopt a deep learning-based semantic segmentation approach to address these problems. Specifically, we use an efficient method to synthesize 30000 well-annotated images containing diverse barcode labels, and get Barcode-30 k, a large-scale dataset with accurate pixel-level annotated barcode in the wild. Moreover, to locate barcode more precisely, we further propose an Effective Barcode Hunter - BarcodeNet. It is a semantic segmentation model based on CNN (Convolutional Neural Network) and is mainly formed with two novel modules, Prior Pyramid Pooling Module (P3M) and Pyramid Refine Module (PRM). Additional ablation studies further demonstrate the effectiveness of BarcodeNet, and it yields a high mIoU result of 95.36% on the proposed synthetic Barcode-30 k validation-set. To prove the practical value of the whole system, we test the BarcodeNet trained on train-set of Barcode-30 k on a manually annotated testing set that only collected from cameras, it achieves mIoU of 90.3%, which is a very accurate result for practical applications.

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Author information

Authors and Affiliations

  1. School of Software and Microelectronics, Peking University, Beijing, China

    Feng Ni & Xixin Cao

Authors
  1. Feng Ni
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  2. Xixin Cao
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Corresponding authors

Correspondence to Feng Ni or Xixin Cao .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Ni, F., Cao, X. (2020). Effective Barcode Hunter via Semantic Segmentation in the Wild. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_35

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  • DOI: https://doi.org/10.1007/978-3-030-37731-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37730-4

  • Online ISBN: 978-3-030-37731-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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