A QR code identification technology in package auto-sorting system

Transcription

1 Modern Physics Letters B Vol. 31, Nos (2017) (5 pages) c World Scientific Publishing Company DOI: /S A QR code identification technology in package auto-sorting system Yi-Juan Di, Jian-Ping Shi and Guo-Yong Mao School of Electric & Photoelectronic Engineering, Changzhou Institute of Technology, Changzhou , China poiuty@163.com shijp@czu.cn maogy@czu.cn Received 6 September 2016 Published 16 May 2017 Traditional manual sorting operation is not suitable for the development of Chinese logistics. For better sorting packages, a QR code recognition technology is proposed to identify the QR code label on the packages in package auto-sorting system. The experimental results compared with other algorithms in literatures demonstrate that the proposed method is valid and its performance is superior to other algorithms. Keywords: QR code recognition; image binarization; package auto-sorting; induction subsystem. 1. Introduction With the rapid development of E-business, the logistics in China keeps growth strongly. The data from State Post Bureau shows that there are about 20.6 billion express packages in The express packages in 2016 are expected to increase 34%. With the rapid expansion of logistics, the sorting operation of packages is very important. Traditional manual sorting operation is not highly efficient and is not suitable for the rapid development of Chinese logistics. Thus, it is necessary to study package auto-sorting technology. In the process of auto-sorting packages, the first important part is to recognize the label on the package. The bar code label is widely used because of its advantages of simple operation and fast information acquisition. However, comparing with QR code, bar code label has shortcomings, such as small capacity of data and poor security. Therefore, in this paper, an improved QR code identification method is presented to recognize the QR code label on package auto-sorting system so as to improve the efficiency and reliability of package auto-sorting. Corresponding author

2 Y.-J. Di, J.-P. Shi & G.-Y. Mao Fig Package Auto-Sorting System Package auto-sorting system. The package auto-sorting system 1 consists of package supply subsystem, induction subsystem, package transmission subsystem and bins subsystem. The package autosorting system in this research was designed as shown in Fig. 1. In the auto-sorting system, the packages were firstly supplied to induction subsystem by the package supply subsystem. Then, packages were recognized and transmitted to empty transport trolleys of transmission subsystem sequentially. Finally, in the continuous circular transmission, packages are sent to different bins according to package s address. In induction subsystem, QR code identification technology was used to identify the QR code labels of supplied packages. The induction subsystem is a key part in the package auto-sorting system and it affects the performance and efficiency of package auto-sorting. 3. Improved QR Code Recognition Technology in Induction Subsystem In induction subsystem, QR code identification technologyis conducted in two steps. The first step is to correct the shape deformation of QR code image. The next step is the image binarization to correct QR code QR code image correction In the process of image acquisition, QR code image is deformed because of the shooting angle. Thus, it is necessary to correct the QR code image. The main steps of correction 2 are shown as Fig

3 A QR code identification technology in package auto-sorting system Original QR Code Image Input QR Key Point Acquirement Inverse Perspective Transformation Normal Image Output Fig. 2. The flowchart of QR code correction under complex background. When the QR code image is inputted, the corner detection method is used to detect QE code key points and find the corresponding points coordinates. Then, the inverse perspective transformation method is utilized to correct the QR code image according to key points. Finally, normal image can be obtained Improvement technology for image binarization The binarization methods can be divided into two categories 3 : global binarization method and local binarization method. The global binarization method, i.e. Otsu s algorithm, 4 can be expressed as follows: { 0, if Gf (x, y) G T, G b (x, y) = (1) 255, if G f (x, y) > G T, where G f (x, y) is the pixel gray value at pixel point (x, y), G b (x, y) is the binarization result of pixel gray value, and G T is the global threshold value between (0, 255). In realistic image binarization, the well-known global binarization method has problems in processing the uneven illumination image. Thus, some local binarization methods, such as Niblack s algorithm, 5 Sauvola s algorithm, 6 improved Wellner s algorithm 7 and Zhang s algorithm 7 are investigated. However, these algorithms still have shortcomings in the recognition of complex uneven illumination image. Therefore, in this research, an improved local binarization method based on Wellner s algorithm is presented. In the proposed method, variable G T is calculated within a window of size w as follows: G T (x, y) = f w w (max w w min w w ) w w (max w w + min w w ), (2) where G T is the local threshold at the central point (x, y) within the w w window, f w w is the sum of the gray values of all pixels within the w w window, and max w w and min w w are the maximum and minimum gray values within the w w window

4 Y.-J. Di, J.-P. Shi & G.-Y. Mao 4. Experimental Results In order to illustrate the performance of the proposed method, the original image was identified by the proposed method and other algorithms by MATLAB 2010a. The original image and experimental results under different algorithms are shown in Fig. 3. (a) Original image (b) Otsu s algorithm (c) Niblack s algorithm (d) Sauvola s algorithm (e) Zhang s algorithm (f) Improved Wellner s algorithm (g) Proposed method Fig. 3. The original image and experimental results under different algorithms. Figure 3 shows that the proposed method is more efficient in recognizing the QR code image and can obtain the clearest and most accurate binarization results. The comparison results show that the proposed method outperforms other approaches. 5. Conclusion In this research, an improved QR code recognition technology is presented for image recognition in the package auto-sorting system. Compared with other algorithms reported in literatures, the simulation results demonstrate that the proposed method has better performance in QR code image identification and its performance and effectiveness are superior over other approaches. Acknowledgments This work was supported by Jiangsu Science and Technology Plan Project under Grant No. BY , Jiangsu 333 High-level Talents Project under Grant No. BRA and Nature Foundation project of Changzhou Institute of Technology under Grant No. YN

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