The invention relates to an impregnated
paper production quality detection method and
system based on
deep learning, and the method comprises the following steps: S1, obtaining an industrial
camera image flow, sensor and process parameters, and event and work order information, and constructing a synchronous data packet; s2, preprocessing the synchronous data packet to obtain preprocessed image data, process data and an ROI
mask; s3, obtaining a detection frame set and a segmentation
mask set based on multi-model AI detection and segmentation according to the preprocessed image data, the process data and the ROI
mask; s4, segmenting the
mask set according to the detection frame set, and performing track clustering along the paper feeding direction to form a defect event flow; s5, on the basis of the defect
event stream, performing online quality prediction and
root cause analysis, and obtaining future interval quality prediction and key factor sorting; and S6, according to future interval quality prediction and key factor sorting, adaptive
process optimization is carried out, and an
optimal control instruction is obtained. According to the invention, the detection accuracy and
fineness are effectively improved, and intelligent identification and cross-frame tracking of defects are realized.