Detection and Quantification of Visual Tablet Surface Defects by Combining Convolutional Neural Network-Based Object Detection and Deterministic Computer Vision Approaches

Tablet surface defects are typically controlled by visual inspection in the pharmaceutical industry. This is an insufficient response variable for knowledge-based formulation and process development, and it results in rather limited robustness of the control strategy. In this article, we present an analytical method for the quantitative characterization of visual tablet surface defects. The method involves analysis of the tablet surface by a digital microscope to obtain optical images and three-dimensional surface scans. Pre-processing procedures are applied for the simplification of the data to allow the detection of the imprint characters and tablet surface structures by a Faster R-CNN object detection model. Geometrical variables like perimeter and area were derived from the results of the object detection model and statistically analyzed for a selected number of tablets. The analysis allowed the development of product-specific acceptance criteria by a small reference dataset, and the quantitative evaluation of sticking, picking, chipping, and abrasion defects. The method showed high precision and sensitivity and demonstrated robust detection of visual tablet surface defects without false negative results. The image analysis was automated, and the developed algorithm can be operated by a simple routine on a standard computer in a few minutes. The method is suitable for industrial use and enables advancements in industrial formulation and process development while providing a novel opportunity for the quality control of visual tablet surface defects.