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| Design and Research of Deep Learning-based Paper Defect Detection System |
| Received:February 23, 2024 |
| DOI：10.11980/j.issn.0254-508X.2024.08.019 |
| Key Words:paper defect detection deep learning system design architecture design |
| Fund Project:浙江省高等学校国内访问工程师“校企合作项目”（FG2023285）；浙江省教育厅一般项目（Y202351406）；嘉兴市应用性基础研究项目（2023AY11022，2024AD10063）。 |
| Author Name | Affiliation | Postcode | | GU Wenjun | Jiaxing Vocational and Technical College, Jiaxing, Zhejiang Province, 314036
Jiaxing Key Lab of Industrial Internet Security, Jiaxing, Zhejiang Province, 314036 | 314036 | | TAN Yongtao | Minfeng Special Paper Co., Ltd., Jiaxing, Zhejiang Province, 314000 | 314000 | | LI Qiang | Jiaxing Vocational and Technical College, Jiaxing, Zhejiang Province, 314036
Jiaxing Key Lab of Industrial Internet Security, Jiaxing, Zhejiang Province, 314036 | 314036 | | LIU Yaobin | Minfeng Special Paper Co., Ltd., Jiaxing, Zhejiang Province, 314000 | 314000 | | ZHOU Yi | Jiaxing Vocational and Technical College, Jiaxing, Zhejiang Province, 314036
Jiaxing Key Lab of Industrial Internet Security, Jiaxing, Zhejiang Province, 314036 | 314036 | | WANG Pingjun | Minfeng Special Paper Co., Ltd., Jiaxing, Zhejiang Province, 314000 | 314000 | | SUN Xia | Jiaxing Vocational and Technical College, Jiaxing, Zhejiang Province, 314036
Jiaxing Key Lab of Industrial Internet Security, Jiaxing, Zhejiang Province, 314036 | 314036 | | LU Wenrong | Zhejiang Paper Industry Association, Hangzhou, Zhejiang Province, 310000 | 310000 | | WU Yuhao | Jiaxing Vocational and Technical College, Jiaxing, Zhejiang Province, 314036
Jiaxing Key Lab of Industrial Internet Security, Jiaxing, Zhejiang Province, 314036 | 314036 | | WU Muyuan | Jiaxing Vocational and Technical College, Jiaxing, Zhejiang Province, 314036
Jiaxing Key Lab of Industrial Internet Security, Jiaxing, Zhejiang Province, 314036 | 314036 |
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| Abstract:A deep learning-based paper defect detection system was designed in this paper to enhance the quality control of papermaking production. This system adopted the architecture model of “CCD + FPGA + industrial control computer + training computer”， achieving real-time collection of paper image data， real-time assessment of paper defects， and real-time identification of types of paper defects. Considering both classification accuracy and inference speed， the MobileNet model was chosen to achieve a classification accuracy of 99.5%. It could infer approximately 103.1 images per second with a resolution of 224×224， meeting the real-time requirements for on-site and recognition of pager defect image classification. |
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