An Accurate and Efficient 1-D Barcode Detector for Medium of Deployment in IoT Systems

Abstract

Camera-based 1-D barcode detectors have a lot of applications in Internet-of-Things (IoT) systems (e.g., retail, air travel, post and parcel services, and manufacturing). Based on the observation that 1-D barcodes always come with a 12-digit product code, this article proposes an end-to-end trainable and fully convoluted model that can detect and output accurate localization results of 1-D barcode and product code simultaneously. Our method uses dilated convolutions-based feature extractors which are then combined with systematic feature merging layers to create a U-shaped network. It predicts multichannel feature maps which later yield localization results after thresholding with a confidence map generated by the model and nonmaximum suppression. Furthermore, we use a Taylor series expansion-based criterion to rank and eliminate a subset of least important convolutional filters of the model, which further increases the inference speed to a great extent. This model can act as a preprocessing module for camera-based barcode decoders. Experimental results on the data set combined from public data sets and self-collected retail products data set obtained in more challenging environment conditions demonstrate that our strategy is effective in increasing the decoding rate of existing commercial barcode decoders efficiently.