Year: 2025
Type: article
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 anHX711 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.
Authors: Sana Arshad, Zainab Mustafa, Noman Mansoor, Shaiza Kamran, Mariam Syed
Institution: NED University of Engineering and Technology
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7083017239
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