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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 NameAffiliationPostcode
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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