Fast, Accurate Barcode Detection In Ultra High Resolution Images


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 in- efficient and computationally expensive. In this paper, we propose using semantic segmentation to achieve a fast and accurate detec- tion of barcodes of various scales in UHR images. Our pipeline involves a modified Region Proposal Network (RPN) on images of size greater than 10k×10k and a newly proposed Y-Net segmenta- tion 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× faster than YOLOv4 and 5.9× faster than Mask R-CNN. In terms of ac- curacy, 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 here.

In IEEE International Conference on Image Processing
Jerome Quenum
Ph.D. student in Electrical Eng & Comp Sci.