  Digital signs(such as barcode or QR code) are widely used in our daily life,
and for many applications, we need to localize them on images. However,
difficult cases such as targets with small scales, half-occlusion, shape
deformation and large illumination changes cause challenges for conventional
methods. In this paper, we address this problem by producing a large-scale
dataset and adopting a deep learning based semantic segmentation approach.
Specifically, a synthesizing method was proposed to generate well-annotated
images containing barcode and QR code labels, which contributes to largely
decrease the annotation time. Through the synthesis strategy, we introduce a
dataset that contains 30000 images with Barcode and QR code - Barcode-30k.
Moreover, we further propose a dual pyramid structure based segmentation
network - BarcodeNet, which is mainly formed with two novel modules, Prior
Pyramid Pooling Module(P3M) and Pyramid Refine Module(PRM). We validate the
effectiveness of BarcodeNet on the proposed synthetic dataset, and it yields
the result of mIoU accuracy 95.36\% on validation set. Additional segmentation
results of real images have shown that accurate segmentation performance is
achieved.
