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| Algorithm of Paper Defect Detection Based on Connected-component Labeling |
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| DOI：10.11981/j.issn.1000-6842.2018.02.51 |
| Key Words:connected-component labeling; run-based method; shape feature extraction; paper defect detection |
| Fund Project:国家自然基金项目（61601271， 61471227， 61603234）；陕西省科技项目（2016SF-444）；陕西省教育厅科研项目（16JK1087）。 |
| Author Name | Affiliation | | ZHAO Xiao1， 2 | 1. Artificial Intelligence Institute， College of Electrical and Information Engineering， Shaanxi University of Science ＆ Technology， Xi’an， Shaanxi Province， 710021
2. Faculty of Information Science and Technology， Aichi Prefectural University， Aichi， Japan， 4801198 | | HE Li-feng1，* | 1. Artificial Intelligence Institute， College of Electrical and Information Engineering， Shaanxi University of Science ＆ Technology， Xi’an， Shaanxi Province， 710021 | | YAO Bin1 | 1. Artificial Intelligence Institute， College of Electrical and Information Engineering， Shaanxi University of Science ＆ Technology， Xi’an， Shaanxi Province， 710021 | | GAO Qi-hang1 | 1. Artificial Intelligence Institute， College of Electrical and Information Engineering， Shaanxi University of Science ＆ Technology， Xi’an， Shaanxi Province， 710021 | | YANG Yun1 | 1. Artificial Intelligence Institute， College of Electrical and Information Engineering， Shaanxi University of Science ＆ Technology， Xi’an， Shaanxi Province， 710021 |
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| Abstract: |
| In order to enhance the accuracy and efficiency of paper defect detection， this paper presented a run-based connected-component labeling algorithm， which could obtain the features of paper defect regions during the connected component was labeled. According to the regions of paper defects are simple connected image， we used a run-based connected-component labeling to record the intermediate results related to the image shape feature extraction during the labeling processing， and combined the labeled results and shape feature values to realize the paper defect detection rapidly. This algorithm could reduce the times of scanning images because it optimized the process of connected-component labeling and shape feature extraction. The experimental results demonstrated that the proposed algorithm could achieve an accurate and efficient detection result for general paper defects， and easily be used in the actual paper defect detection system． |
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