Tablet Guard: Load Cell based Quality Assurance with Image Processing

Sana Arshad (corresponding author); Zainab Mustafa; Noman Mansoor; Shaiza Kamran; Mariam Syed

Authors

Sana Arshad (corresponding author) NED University of Engineering and Technology, Karachi
Zainab Mustafa NED University of Engineering and Technology
Noman Mansoor NED University of Engineering and Technology
Shaiza Kamran NED University of Engineering and Technology
Mariam Syed NED University of Engineering and Technology

Keywords:

Artificial Intelligence, YOLO, Deep Learning and Arduino

Abstract

Every week, the pharmaceutical business manufactures thousands of pills, each of which must be thoroughly checked before being distributed to customers. The proposed Tablet-Guard project addresses this issue through innovative integration of multiple advanced technologies including load cell technology, artificial intelligence, and a servo motor-based removal mechanism for pharmaceutical quality assurance. The system incorporates deep learning-based image processing, coupled with a load cell using an HX711 module to inspect and assess the quality of each tablet in a blister strip as it moves along the conveyor belt. It inspects defects including irregular shapes and incomplete blister strips. The utilization of YOLOv8 enables real-time defect detection with high accuracy (mAP of 0.995), enhancing efficiency and minimizing production line disruptions. By accurately detecting and addressing defects such as broken, missing, or cracked tablets within blister strips, the system significantly minimizes the likelihood of substandard products being distributed to consumers.

Author Biographies

Zainab Mustafa, NED University of Engineering and Technology

Ms. Zainab Mustafa is ex-student of Department of Electronic Engineering, batch 2020. Her seat/ registration number was EL-20003.

Noman Mansoor, NED University of Engineering and Technology

Mr. Noman Mansoor is ex-student of Department of Electronic Engineering, batch 2020. His seat/ registration number was EL-20017.

Shaiza Kamran, NED University of Engineering and Technology

Ms. Shaiza Kamran, is ex-student of Department of Electronic Engineering, batch 2020. Her seat/ registration number was EL-20024.

Mariam Syed, NED University of Engineering and Technology

Ms. Mariam Syed, is ex-student of Department of Electronic Engineering, batch 2020. Her seat/ registration number was EL-20025.

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