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. 2019 Dec 11;19(24):5466.
doi: 10.3390/s19245466.

Adaptive Binarization of QR Code Images for FastAutomatic Sorting in Warehouse Systems

Rongjun Chen  1   2 , Yongxing Yu  1 , Xiansheng Xu  1 , Leijun Wang  1 , Huimin Zhao  1 , Hong-Zhou Tan  2
Affiliations

Adaptive Binarization of QR Code Images for FastAutomatic Sorting in Warehouse Systems

Rongjun Chen et al. Sensors (Basel). .

Abstract

As the fundamental element of the Internet of Things, the QR code has become increasingly crucial for connecting online and offline services. Concerning e-commerce and logistics, we mainly focus on how to identify QR codes quickly and accurately. An adaptive binarization approach is proposed to solve the problem of uneven illumination in warehouse automatic sorting systems. Guided by cognitive modeling, we adaptively select the block window of the QR code for robust binarization under uneven illumination. The proposed method can eliminate the impact of uneven illumination of QR codes effectively whilst meeting the real-time needs in the automatic warehouse sorting. Experimental results have demonstrated the superiority of the proposed approach when benchmarked with several state-of-the-art methods.

Keywords: QR code; adaptive binarization; automatic sorting system; uneven illumination.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Diagram of warehouse automatic sorting system.
Figure 2
Figure 2
Diagram of preprocessing.
Figure 3
Figure 3
Process of the adaptive selection of the block window.
Figure 4
Figure 4
The experimental results in ascending order of image size. (a) Original image; (b) Block size w – 10; (c) Block size w  – 5; (d) Block size w; (e) Block size w  + 5; (f) Block size w  + 10.
Figure 5
Figure 5
The values of PSNR and SSIM of different sizes of QR codes under different windows processing. (a) The line graph corresponding to PSNR; (b) The line graph corresponding to SSIM.
Figure 6
Figure 6
The experimental results of fixed image size. (a) Original image; (b) Block size w – 10; (c) Block size w  – 5; (d) Block size w; (e) Block size w  + 5; (f) Block size w + 10.
Figure 7
Figure 7
The values of PSNR and SSIM of QR codes in the same size under different window processing. (a) The line graph corresponding to PSNR; (b) The line graph corresponding to SSIM.
Figure 8
Figure 8
Process of QR code binarization.
Figure 9
Figure 9
Process of cyclic threshold algorithm.
Figure 10
Figure 10
The original image and experimental results from different algorithms: (a) Original image; (b) Otsu’s algorithm; (c) Niblack’s algorithm; (d) Yao’s algorithm; (e) Di’s algorithm; (f) Proposed method.
Figure 11
Figure 11
The original image and experimental results from different algorithms: (a) Original image; (b) Otsu’s algorithm; (c) Niblack’s algorithm; (d) Yao’s algorithm; (e) Di’s algorithm; (f) Proposed method.
Figure 12
Figure 12
The original image and experimental results from different algorithms: (a) Original image; (b) Otsu’s algorithm; (c) Niblack’s algorithm; (d) Yao’s algorithm; (e) Di’s algorithm; (f) Proposed method.
Figure 13
Figure 13
The original image and experimental results from different algorithms: (a) Original image; (b) Otsu’s algorithm; (c) Niblack’s algorithm; (d) Yao’s algorithm; (e) Di’s algorithm; (f) Proposed method.
Figure 14
Figure 14
The original image and experimental results from different algorithms: (a) Original image; (b) Otsu’s algorithm; (c) Niblack’s algorithm; (d) Yao’s algorithm; (e) Di’s algorithm; (f) Proposed method.
Figure 15
Figure 15
The values of PSNR and SSIM of different QR codes using different algorithms. (a) The line graph corresponding to PSNR; (b) The line graph corresponding to SSIM.
See this image and copyright information in PMC

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