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Fine localization and distortion resistant detection of multi-class barcode in complex environments

机译:复杂环境中多级条形码的细定位和抗扭检测

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摘要

Barcode, including one-dimensional (1D) barcode and two-dimensional (2D) barcode, can be seen almost anywhere in our lives. In many barcode-based mobile systems, different barcodes will appear simultaneously with different angles, shapes, and image quality. Barcode localization is a significant prerequisite for barcode decoding in these applications. In this paper, we propose a region-based end-to-end network to finely localize and classify 1D barcode and Quick Response (QR) code in complex environments. Two special layers are designed in our network. One is a quadrilateral regression layer to localize arbitrary quadrilateral bounding boxes, and another is a Multi-scale Spatial Pyramid Pooling (MSPP) layer to improve the detection accuracy of small-scale barcodes. Extensive experiments on existing public datasets and our own dataset have verified the effectiveness of proposed layers. We also demonstrate that our method can resist some distortions by simulating barcode images of different image qualities. What's more, a human decoding experiment is also performed to prove the effectiveness of our method as a preprocessor for QR code decoding.
机译:条形码,包括一维(1D)条形码和二维(2D)条形码,可以在我们的生活中的任何地方看到。在许多基于条形码的移动系统中,不同的条形码将同时出现不同的角度,形状和图像质量。条形码定位是这些应用中的条形码解码的重要前提。在本文中,我们提出了一个基于区域的端到端网络,以在复杂环境中精细定位和分类1D条形码和快速响应(QR)代码。我们的网络设计了两层特殊图层。一个是四边形回归层来定位任意四边形边界框,另一个是多尺度空间金字塔池(MSPP)层,以提高小尺度条形码的检测精度。对现有公共数据集的广泛实验和我们自己的数据集已经验证了所提出的层的有效性。我们还证明我们的方法可以通过模拟不同图像质量的条形码图像来抵抗一些扭曲。更重要的是,还执行人类解码实验,以证明我们的方法作为QR码解码的预处理器的有效性。

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