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Updated: Jun 8, 2025
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Automated tablet defect detection and the prediction of disintegration time and crushing strength with deep learning
Anna Diószegi1, Máté Ficzere1, Lilla Alexandra Mészáros1
1Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rakpart 3, H-1111 Budapest, Hungary.
Abstract:
This paper presents novel measurement methods, where deep learning was used to detect tableting defects and determine the crushing strength and disintegration time of tablets on images captured by machine vision. Five different classes of defects were used and the accuracy of the real-time defect recognition performed with the deep learning algorithm YOLOv5 was 99.2 %. The system can already match the production capability of tablet presses, with still further room left for improvement. The YOLOv5 algorithm was also used to determine the disintegration time and crushing strength of tablets produced at different compression force settings based on their surface texture. With these accurate, low-cost methods, the 100 % screening of the produced tablets could be carried out, resulting in the improvement of quality control and effectiveness of pharmaceutical production.
