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Research on Quick Response Code Defect Detection Algorithm.

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Bibliographic Details
Title: Research on Quick Response Code Defect Detection Algorithm.
Authors: Guo Yanhua, Zhou Sihua, Zhou Xiaodong, Chen Bojun, Wang Shaohui
Source: Cybernetics & Information Technologies; Mar2017, Vol. 17 Issue 1, p135-145, 11p
Subject Terms: TWO-dimensional bar codes, AUTOMATIC identification, DATA transmission systems
Abstract: Defect Detection is one of the most important parts of Automatic Identification and Data transmission. Quick Response code (QR code) is one of the most popular types of two-dimensional barcodes. It is a challenge to detect defect of various QR code images efficiently and accurately. In this paper, we propose the procedure by a serial of carefully designed preprocessing methods. The defect detection procedure consists of QR code identification, QR code reconstruction, perspective transformation, image binarization, morphological operation, image matching, and Blob analysis. By these steps, we can detect defect of different types of QR code images. The experiment results show that our method has stronger robustness and higher efficiency. Moreover, experiment results on QR code images show that the prediction accuracy of proposed method reaches 99.07% with an average execution time of 6.592 ms. This method can detect defect of these images in real time. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Defect Detection is one of the most important parts of Automatic Identification and Data transmission. Quick Response code (QR code) is one of the most popular types of two-dimensional barcodes. It is a challenge to detect defect of various QR code images efficiently and accurately. In this paper, we propose the procedure by a serial of carefully designed preprocessing methods. The defect detection procedure consists of QR code identification, QR code reconstruction, perspective transformation, image binarization, morphological operation, image matching, and Blob analysis. By these steps, we can detect defect of different types of QR code images. The experiment results show that our method has stronger robustness and higher efficiency. Moreover, experiment results on QR code images show that the prediction accuracy of proposed method reaches 99.07% with an average execution time of 6.592 ms. This method can detect defect of these images in real time. [ABSTRACT FROM AUTHOR]
ISSN:13119702
DOI:10.1515/cait-2017-0011