  Object detection in Ultra High-Resolution (UHR) images has long been a
challenging problem in computer vision due to the varying scales of the
targeted objects. When it comes to barcode detection, resizing UHR input images
to smaller sizes often leads to the loss of pertinent information, while
processing them directly is highly inefficient and computationally expensive.
In this paper, we propose using semantic segmentation to achieve a fast and
accurate detection of barcodes of various scales in UHR images. Our pipeline
involves a modified Region Proposal Network (RPN) on images of size greater
than 10k$\times$10k and a newly proposed Y-Net segmentation network, followed
by a post-processing workflow for fitting a bounding box around each segmented
barcode mask. The end-to-end system has a latency of 16 milliseconds, which is
$2.5\times$ faster than YOLOv4 and $5.9\times$ faster than Mask RCNN. In terms
of accuracy, our method outperforms YOLOv4 and Mask R-CNN by a $mAP$ of 5.5%
and 47.1% respectively, on a synthetic dataset. We have made available the
generated synthetic barcode dataset and its code at
http://www.github.com/viplab/BSBD/.
