TY - GEN
T1 - Real-time Tablet Inspection using Computer Vision for Blister Packing Machines
AU - Karthikeyan Pillai, Anish Kadamathikuttiyil
AU - Ellis, Ashley
AU - Rossato, Helkier Henrique
AU - Fallon, Enda
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The pharmaceutical industry has been grappling with a problem of wastage of tablets during blister packing due to inspection issues. The current approach does not involve inspecting the tablets before they are placed into blister pockets. Our study aims to address this issue by proposing a solution that involves early identification of defective tablets using a convolutional neural network. The objective is to use a trained convolutional neural network with the help of canny edge detection and TensorFlow with Keras to develop a zero-defect tablet quality control system. We have several test vectors as canny edge can only be used to detect whether tablets are defective or pristine based on size and shape. In this work we test colour without canny edge and then we will iterate on the created model by creating two convolutional neural networks. One will have canny edge and one will not and we will compare their results on defective or pristine tablets. The initial results of our work were that our model could predict certain colour's well and that our canny edge convolutional neural network produced worse accuracy results than the blob detection convolutional neural network. The blob detection convolutional neural network produced good results for detecting defective or pristine tablets.
AB - The pharmaceutical industry has been grappling with a problem of wastage of tablets during blister packing due to inspection issues. The current approach does not involve inspecting the tablets before they are placed into blister pockets. Our study aims to address this issue by proposing a solution that involves early identification of defective tablets using a convolutional neural network. The objective is to use a trained convolutional neural network with the help of canny edge detection and TensorFlow with Keras to develop a zero-defect tablet quality control system. We have several test vectors as canny edge can only be used to detect whether tablets are defective or pristine based on size and shape. In this work we test colour without canny edge and then we will iterate on the created model by creating two convolutional neural networks. One will have canny edge and one will not and we will compare their results on defective or pristine tablets. The initial results of our work were that our model could predict certain colour's well and that our canny edge convolutional neural network produced worse accuracy results than the blob detection convolutional neural network. The blob detection convolutional neural network produced good results for detecting defective or pristine tablets.
UR - http://www.scopus.com/inward/record.url?scp=85185196095&partnerID=8YFLogxK
U2 - 10.1109/ICCA59364.2023.10401685
DO - 10.1109/ICCA59364.2023.10401685
M3 - Conference contribution
AN - SCOPUS:85185196095
SN - 9798350303254
T3 - ICCA 2023 - 2023 5th International Conference on Computer and Applications, Proceedings
BT - ICCA 2023 - 2023 5th International Conference on Computer and Applications, Proceedings
A2 - Alja'Am, Jihad Mohamad
A2 - Alja'Am, Jihad Mohamad
A2 - Elseoud, Samir Abou
A2 - Karam, Omar
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Computer and Applications, ICCA 2023
Y2 - 28 November 2023 through 30 November 2023
ER -