Open AccessDesign of Barcode Recognition System Based on YOLOV5
Author(s) -
Xiaoyan Zhu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1995/1/012052
Subject(s) - barcode , computer science , affine transformation , code (set theory) , minimum bounding box , artificial intelligence , algorithm , computer vision , chain code , set (abstract data type) , image (mathematics) , mathematics , pure mathematics , programming language , operating system
In this paper, based on the research focus of the current logistics field, a deep learning based express single-side bar code extraction algorithm is designed. This algorithm can recognize the original image and read out the information of the package. The algorithm is divided into two parts: bar code localization algorithm and bar code recognition algorithm. To avoid high delay, we use the YOLOV5S network to complete the barcode localization algorithm. First, the YOLOV5S algorithm is used to frame out the barcode, and the bounding Box is cut off. Then, OpenCV's method is used to obtain the deflection Angle of the image. Finally, affine transformation is used to correct the images with deflection. The bar code recognition algorithm is to use ZBAR algorithm to decode the bar code and output the decoded content in the form of string, and finally realize the location and recognition of the bar code on the single side of the express. Finally, through the test of the data set, it can be seen from the experimental results that this paper has accomplished the goal well.