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| Machine Vision Based Online Diagnosis Methods for Web Surfacial Defects: A Review |
| Received:March 20, 2025 Revised:April 16, 2025 |
| DOI：10.11981/j.issn.1000-6842.2026.01.174 |
| Key Words:online paper defect diagnosis;machine vision;image preprocessing algorithms;paper defect determination algorithms;paper defect recognition algorithms |
| Fund Project:国家自然科学基金（62073206）；西安市科技计划项目（2020KJRC0146）。 |
| Author Name | Affiliation | Postcode | | TANG Wei* | 1College of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an ， Shaanxi Province， 710021 | 710021 | | ZHOU Guoqing* | 1College of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an ， Shaanxi Province， 710021 | 710021 | | LIU Yan | 1College of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an ， Shaanxi Province， 710021 | 710021 | | KANG Jie | 1College of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an ， Shaanxi Province， 710021 | 710021 | | WANG Mengxiao | 2Shaanxi Xiwei Process Automation Engineering Co.， Ltd.， Xianyang， Shaanxi Province， 712081 | 712081 |
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| Abstract: |
| This paper focused on online diagnosis methods for surfacial defects in specialty paper. The fundamental principles and general workflow of online paper defect diagnosis based on machine vision technology were outlined. A comprehensive review of key technologies was provided， including hardware architecture and core equipment for real-time acquisition of paper image data， image data preprocessing algorithms （mainly including feature extraction， image enhancement， and image decomposition and reconstruction algorithms）， paper defect determination algorithms （mainly including grayscale feature-based， morphological feature-based， and deep learning-based algorithms）， and paper defect recognition algorithms （mainly including feature analysis-based and machine learning-based algorithms）. Additionally， this paper introduced performance evaluation metrics and post-processing techniques for online paper defect diagnosis algorithms. Finally， it analyzed the key issues and challenges in current industrial applications of web defect diagnosis technology and provided insights into future development trends. |
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