Ink jet nozzle clogging is a common problem that occurs in the process of digital ink-jet printing.Real-time detection of ink-jet printing images is necessary to identify and promptly avoid the scrapping of a large number of products during the production process.Given the inefficiency of the current traditional methods for defect detection in digital ink jet printing and the imbalance between detection speed and accuracy,a lightweight semantic segmentation defect detection method had been proposed.The backbone network structure of Deeplabv3+was improved using GhostNet,three residual modules were added to the ASPP module in Deeplabv3+,the context structure of the cavity convolution was adjusted,and the Focal Loss loss function and ReLU6 activation function were utilized.The experimental results demonstrated that the improved model,Gh-R-Deeplabv3+,processed 47.71 frames per second on the digital ink jet printing defect data set.It achieved an average cross merge ratio of 82.8%and an average pixel accuracy of 90.96%.The model was confirmed to have achieved a high detection speed and accuracy,conforming to the real-time detection standard.It was confirmed that a relative balance between detection speed and accuracy for digital ink jet printing defects is achieved by the improved model in this study.
Digital ink jet printingDefect detectionDeeplabv3+GhostNet
NETL
NSTL
万方数据
