This paper presents a system, where images acquired with a digital camera are coupled
with image analysis and deep learning to identify and categorize film coating defects
and to measure the film coating thickness of tablets. There were 5 different classes
of defective tablets, and the YOLOv5 algorithm was utilized to recognize defects,
the accuracy of the classification was 98.2%. In order to characterize coating thickness,
the diameter of the tablets in pixels was measured, which was used to measure the
coating thickness of the tablets. The proposed system can be easily scaled up to match
the production capability of continuous film coaters. With the developed technique,
the complete screening of the produced tablets can be achieved in real-time resulting
in the improvement of quality control.
