Paper Info
Title
EMBDN: An Efficient Multiclass Barcode Detection Network for Complicated Environments
Abstract
This article presents a novel method for efficient barcodes detection in real and complicated environments using a convolutional neural network (CNN)-based model. The method is developed as a preprocess-module of existing decoders to enhance decoding rates. Our method is trained as an end-to-end model to determine accurate locations of four barcode vertexes. Our method consists of four modules: 1) base net module; 2) region proposals generator; 3) classification and regression module; and 4) distortion removal module. The feature of barcodes extracted from the base net is fed to the next module. Region proposals are generated and selected as region of interest (ROI). Then the ROI are forward propagated to the classification and regression module to determine the positions and shapes of the barcodes. Finally, the distortion removal module is used to remove the geometric distortion according to regression parameters acquired from the previous step. The accurate position and distorted barcodes shape can be determined and corrected by our method. We validate our method on a challenging large-scale dataset in experiments. Compared with the previous methods, our method provides an end-to-end solution to determine accurate locations of barcode vertexes, which shows an excellent performance on detection accuracy. In addition, our method can enhance decoding rate through distortion removal.
Year
DOI
Venue
2019
10.1109/JIOT.2019.2933254
IEEE Internet of Things Journal
Keywords
DocType
Volume
Distortion,Decoding,Feature extraction,Cameras,Internet of Things,Proposals,Two dimensional displays
Journal
6
Issue
ISSN
Citations 
6
2327-4662
3
PageRank 
References 
Authors
0.42
0
8
Name
Order
Citations
PageRank
Jun Jia142.46
Guangtao Zhai21707145.33
Jiahe Zhang352.14
Zhongpai Gao4193.55
Zehao Zhu531.44
Xiongkuo Min633740.88
Xiaokang Yang73581238.09
Guodong Guo82548144.00