Fine Detection and Classification of Multi-class Barcode in Complex Environments

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 use a region-based end-to-end network with a quadrilateral regression layer to finely localize and classify 1D barcode and Quick Response (QR) code. In addition, we use a multi-scale feature fusion layer to improve the detection accuracy of small scale barcode in complex environments. Extensive experiments on existing public datasets and our own dataset demonstrate the effectiveness of the proposed method.