An Enhanced Deep Learning-Based Pharmaceutical Crystal Detection with Regional Filtering

Abstract

In the process of pharmaceutical crystallization, the automatic detection of crystal shapes in images is important since controlling the morphology of the crystals improves the quality of pharmaceutical crystals. In this paper, a novel image detection method called RECDet is proposed. It leverages an automatically adapted binary image to bypass background regions, thereby reducing the detection field. In addition, the method greatly reduces the training time while improving the detection accuracy by using a specially designed detection box for the crystal shape. The performance of our model is evaluated through experimental analysis on a publicly available glutamate crystal dataset and a self-made entecavir pharmaceutical crystal dataset. Experimental results show that RECDet improves the accuracy of prediction bounding boxes by more than 2% compared to other popular models and achieves a classification accuracy of 98%. It can be used as a promising tool in the application of pharmaceutical crystallization control.