Enhancing Pharmaceutical Inspection through Poisson Image Editing Techniques

Abstract

This research explores the application of Poisson image editing techniques for enhancing the dataset quality in the detection of defects within pharmaceutical products. The focus is on addressing common defect types that compromise product integrity and safety. By employing Poisson image editing, the researcher aim to improve the accuracy and efficiency of machine learning models used in pharmaceutical quality control. The outcomes indicated substantial enhancements in detecting and classifying defects, thereby promising to elevate the standards of pharmaceutical safety. This study not only underscores the value of advanced image editing in quality assurance processes, but also encourages further exploration into its potential across various aspects of pharmaceutical manufacturing and inspection. Key Terms- Data Augmentation, Image Blending, Pharmaceutical Vials, Quality Assurance.