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A Blind Quality Measure for Industrial 2D Matrix Symbols Using Shallow Convolutional Neural Network
Zhaohui Che1; Guangtao Zhai1; Jing Liu2; Ke Gu3; Patrick Le Callet4; Jiantao Zhou5; Xianming Liu6
2018-09-06
Conference Name25th IEEE International Conference on Image Processing (ICIP)
Source Publication2018 25th IEEE International Conference on Image Processing (ICIP)
Conference Date7-10 Oct. 2018
Conference PlaceAthens, Greece
Abstract

Industrial two-dimensional (2D) matrix symbols are ubiquitous throughout the automatic assembly lines. Most industrial 2D symbols are corrupted by various inevitable artifacts. State-of-the-art decoding algorithms are not able to directly handle low-quality symbols irrespective of problematic artifacts. Degraded symbols require appropriate preprocessing methods, such as morphology filtering, median filtering, or sharpening filtering, according to specific distortion type. In this paper, we first establish a database including 3000 industrial 2D symbols which are degraded by 6 types of distortions. Second, we utilize a shallow convolutional neural network (CNN) to identify the distortion type and estimate the quality grade for 2D symbols. Finally, we recommend an appropriate preprocessing method for low-quality symbol according to its distortion type and quality grade. Experimental results indicate that the proposed method outperforms state-of-the-art methods in terms of PLCC, SRCC and RMSE. It also promotes decoding efficiency at the cost of low extra time spent.

Keyword2d Matrix Symbol Image Quality Assessment Convolutional Neural Network
DOI10.1109/ICIP.2018.8451591
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Software Engineering ; Imaging Science & Photographic Technology
WOS IDWOS:000455181502121
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Faculty of Science and Technology
Affiliation1.Institute of Image Commu. and Network Engin., Shanghai Jiao Tong University, China
2.Tianjin University, China
3.Beijing University of Technology, China
4.Polytech Nantes, France
5.University of Macau, China
6.Harbin Institute of Technology, China
Recommended Citation
GB/T 7714
Zhaohui Che,Guangtao Zhai,Jing Liu,et al. A Blind Quality Measure for Industrial 2D Matrix Symbols Using Shallow Convolutional Neural Network[C],2018.
APA Zhaohui Che,Guangtao Zhai,Jing Liu,Ke Gu,Patrick Le Callet,Jiantao Zhou,&Xianming Liu.(2018).A Blind Quality Measure for Industrial 2D Matrix Symbols Using Shallow Convolutional Neural Network.2018 25th IEEE International Conference on Image Processing (ICIP).
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