Design and Research of Deep Learning-based Paper Defect Detection System
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 real-time 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.
paper defect detectiondeep learningsystem designarchitecture design
万方数据
维普
