Image data set for AI-aided printed line smearing analysis of the roll-to-roll screen printing process for printed electronics

A total of 20 images were collected with cross-directional (CD) printed lines of various line widths and smearing areas using the in-house roll-to-roll screen printing system. Then these images were labeled pixel-wise into three classes: smearing, printed line, and background, and labels were saved as one channel 8-bit images with corresponding intensity 1, 2, and 3. This data set was used to train a U-Net-like deep convolutional neural network (DCNN) to detect continuous printed line smearing defects.