摘要
本课题设计了基于深度学习的纸病检测系统,用于提高造纸生产过程中的质量控制水平。该系统采用了“CCD+FPGA+工业控制计算机+训练计算机”的架构模式,实现了对纸张图像数据的实时采集、纸病的实时判断和纸病类型的实时识别。综合考虑分类准确率与推理速度,选择MobileNet模型算法,其分类准确率达99.5%,每秒可推理约103.1张分辨率为224×224的图像,满足现场纸病图像分类识别的实时要求。
A deep learning-based paper defect detection system was designed in this paper to enhance the quality control of papermaking pro⁃duction.This system adopted the architecture model of“CCD+FPGA+industrial control computer+training computer”,achieving realtime collection of paper image data,real-time assessment of paper defects,and real-time identification of types of paper defects.Consider⁃ing 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 recogni⁃tion of pager defect image classification.
作者
顾文君
谭永涛
李强
刘耀斌
周易
王平军
孙霞
陆文荣
吴昱昊
伍沐原
GU Wenjun;TAN Yongtao;LI Qiang;LIU Yaobin;ZHOU Yi;WANG Pingjun;SUN Xia;LU Wenrong;WU Yuhao;WU Muyuan(Jiaxing Vocational and Technical College,Jiaxing,Zhejiang Province,314036;Minfeng Special Paper Co.,Ltd.,Jiaxing,Zhejiang Province,314000;Jiaxing Key Lab of Industrial Internet Security,Jiaxing,Zhejiang Province,314036;Zhejiang Paper Industry Association,Hangzhou,Zhejiang Province,310000)
出处
《中国造纸》
CAS
北大核心
2024年第8期154-159,共6页
China Pulp & Paper
基金
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)。
关键词
纸病检测
深度学习
系统设计
架构设计
paper defect detection
deep learning
system design
architecture design