Tablet Guard: Load Cell based Quality Assurance with Image Processing

Sana Arshad; Zainab Mustafa; Noman Mansoor; Shaiza Kamran; Mariam Syed

Authors

Sana Arshad 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.

References

& P. S. A. Kamaraj, “Automation in Pharmaceutical Industry and Global Regulatory Compliance,” JCPR. Accessed: Mar. 09, 2025. [Online]. Available: https://jcpr.humanjournals.com/automation-in-pharmaceutical-industry-and-global-regulatory-compliance/

Deepti and R. Bansal, “Enhanced Feature Extraction Technique for Detection of Pharmaceutical Drugs,” Intl. J. Engg. Res. and Gen. Sci., 2015.

A. M. S. Abdur Rehman Riaz, M.U. Farooq, “Tablet Inspection and Sorting Machine,” Int. J. Comput. Appl. Technol., vol. 126, no. 9, 2015, [Online]. Available: https://www.ijcaonline.org/research/volume126/number9/riaz-2015-ijca-906191.pdf

V. Itikala, “Arduino Weighing Machine Using Load Cell and HX711 Module,” SSRN Electron. J., Jul. 2021, doi: 10.2139/SSRN.3918720.

N. S. Arden, A. C. Fisher, K. Tyner, L. X. Yu, S. L. Lee, and M. Kopcha, “Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future,” Int. J. Pharm., vol. 602, p. 120554, 2021, doi: https://doi.org/10.1016/j.ijpharm.2021.120554.

and M. A. V K. T. S., C. Prasad, P. V., “Anomaly detection in pharmaceutical pills using image processing,” International Journal of Advance Research, Ideas and Innovations in Technology. Accessed: Mar. 09, 2025. [Online]. Available: https://www.researchgate.net/publication/359993017_Anomaly_detection_in_pharmaceutical_pills_using_image_processing

H. B. Kekre, D. Mishra, and V. Desai, “Detection of defective pharmaceutical capsules and its types of defect using image processing techniques,” 2014 Int. Conf. Circuits, Power Comput. Technol. ICCPCT 2014, pp. 1190–1195, Mar. 2014, doi: 10.1109/ICCPCT.2014.7055049.

L. Zhang, R. Zhou, Y. Sun, X. Chen, and Z. Yang, “A Defect Detection Algorithm of Round Tablets Based on Image Information,” 2024 9th Int. Conf. Intell. Comput. Signal Process. ICSP 2024, pp. 625–633, 2024, doi: 10.1109/ICSP62122.2024.10743238.

J. Sa, Z. Li, Q. Yang, and X. Chen, “Packaging defect detection system based on machine vision and deep learning,” 2020 5th Int. Conf. Comput. Commun. Syst. ICCCS 2020, pp. 404–408, May 2020, doi: 10.1109/ICCCS49078.2020.9118413.

H. T. Quan, D. D. Huy, N. T. Hoan, and N. T. Duc, “Deep Learning-Based Automatic Detection of Defective Tablets in Pharmaceutical Manufacturing,” IFMBE Proc., vol. 85, pp. 789–801, 2022, doi: 10.1007/978-3-030-75506-5_64.

Prajwala N B, “Defect Detection in Pharma Pills Using Image Processing,” Int. J. Eng. Technol., 2018, doi: 10.14419/IJET.V7I3.3.14497.

S.-H. L. Hyuk-Ju Kwon, Hwi-Gang Kim, “Pill Detection Model for Medicine Inspection Based on Deep Learning,” Chemosensors, vol. 10, no. 1, p. 4, 2022, doi: https://doi.org/10.3390/chemosensors10010004.

J. Liu, M. Zhu, T. Cai, D. Kong, and Y. Xing, “Intelligent Tablet Detection System Based on Capsule Neural Network,” 2022 IEEE 2nd Int. Conf. Power, Electron. Comput. Appl. ICPECA 2022, pp. 665–669, 2022, doi: 10.1109/ICPECA53709.2022.9719182.

D. Yendri, Adrizal, Derisma, and H. Afif, “Design of cow cattle weighing system technology and automatic giving feed,” 2020 Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2020 - Proc., pp. 185–191, Oct. 2020, doi: 10.1109/ICITSI50517.2020.9264977.

R. G. and A. F. J. Redmon, S. Divvala, “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. Accessed: Mar. 09, 2025. [Online]. Available: https://ieeexplore.ieee.org/document/7780460

“YOLOv8 vs SSD: Choosing the Right Object Detection Model,” Keylabs. Accessed: Mar. 23, 2025. [Online]. Available: https://keylabs.ai/blog/yolov8-vs-ssd-choosing-the-right-object-detection-model/

N. Klingler, “RetinaNet: Single-Stage Object Detector with Accuracy Focus,” viso.ai. Accessed: Mar. 23, 2025. [Online]. Available: https://viso.ai/deep-learning/retinanet/

“Achieve Faster Inference Speeds with Ultralytics YOLOv8 and Intel’s OpenVINO,” Ultralytics, [Online]. Available: https://www.ultralytics.com/blog/achieve-faster-inference-speeds-ultralytics-yolov8-openvino.

“Rotated-RetinaNet,” GitHub, [Online]. Available: https://github.com/ming71/Rotated-RetinaNet.

S. G. E. Brucal, L. C. M. de Jesus, and L. A. Samaniego, “Development of a Localized Tomato Leaf Disease Detection using YoloV9 Model via RoboFlow 3.0,” 2024 IEEE 13th Glob. Conf. Consum. Electron., pp. 601–603, Oct. 2024, doi: 10.1109/GCCE62371.2024.10760343.

A. V. Kabir A. Pathak, Prapti Kafle, “Deep learning-based defect detection in film-coated tablets using a convolutional neural network,” Int. J. Pharm., vol. 671, p. 125220, 2025, doi: https://doi.org/10.1016/j.ijpharm.2025.125220.

Z. K. N. Anna Diószegi, Máté Ficzere, Mészáros, Lilla Alexandra, Péterfi, Orsolya, Farkas, Attila, Galata, Dorián László, “Automated tablet defect detection and the prediction of disintegration time and crushing strength with deep learning based on tablet surface images,” Int. J. Pharm., vol. 667, p. 124896, 2024, doi: https://doi.org/10.1016/j.ijpharm.2024.124896.

R. Patgiri, V. Ajantha, S. Bhuvaneswari, and V. Subramaniyaswamy, “Intelligent Defect Detection System in Pharmaceutical Blisters Using YOLOv7,” 2nd Int. Conf. Emerg. Trends Inf. Technol. Eng. ic-ETITE 2024, 2024, doi: 10.1109/IC-ETITE58242.2024.10493735.

H. W. and Z. X. Y. Wang, “Efficient Detection Model of Steel Strip Surface Defects Based on YOLO-V7,” IEEE Access, vol. 10, pp. 133936–133944, 2022, doi: 10.1109/ACCESS.2022.3230894.

S. G. E. Brucal, L. C. M. De Jesus, S. R. Peruda, L. A. Samaniego, and E. D. Yong, “Development of Tomato Leaf Disease Detection using YoloV8 Model via RoboFlow 2.0,” GCCE 2023 - 2023 IEEE 12th Glob. Conf. Consum. Electron., pp. 692–694, 2023, doi: 10.1109/GCCE59613.2023.10315251.

L. A. Samaniego, S. R. Peruda, S. G. E. Brucal, E. D. Yong, and L. C. M. De Jesus, “Image Processing Model for Classification of Stages of Freshness of Bangus using YOLOv8 Algorithm,” GCCE 2023 - 2023 IEEE 12th Glob. Conf. Consum. Electron., pp. 401–403, 2023, doi: 10.1109/GCCE59613.2023.10315381.