Authors : Ram Kumar Sridharan; Anisa Xhafa; Samruddhi Chaodhari; Sreekanth Putsala

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/4vtu3vvw

Scribd : https://tinyurl.com/324ctr9d

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP831

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the pharmaceutical industry, manual counting of vials and pre-filled syringes (PFS) is a time- consuming process prone to human error, which can lead to inventory discrepancies and operational inefficiencies. This research addresses these challenges by automating the counting process using state-of-the-art deep learning techniques. We employ the YOLO (You Only Look Once) architecture from the Ultralytics library, renowned for its real-time object detection capabilities. Our study compares three versions of the YOLO models (v8, v9, v10) to determine the most accurate and efficient model for this application and designed to handle both images and videos. In this study, we applied the Slicing Algorithms for Hyper Inference (SAHI) technique to enhance object detection by efficiently handling smaller objects within larger images, thereby aiming to improve the overall accuracy and robustness of the model. However, our experimental results did not show a significant improvement over existing methods which highlights the potential limitations of the SAHI technique in certain contexts, suggesting the need for further investigation into its effectiveness and adaptability across diverse applications. Using more than 6000 images, the model were trained with a result of high mean average precision of 0.969 showcasing their high detection precision. With a counting accuracy of more than 95%, the proposed model offers an effective solution by eliminating the need for manual counting, thus reducing the potential for human error inherent in traditional methods. Additionally, the developed system seamlessly integrates the counting values with existing inventory management platforms, ensuring up-to-date stock levels and enhancing inventory accuracy. This integration offers substantial time and cost savings for the pharmaceutical and healthcare industries.

Keywords : Artificial Intelligence, Counting Vials and Syringes, Image Preprocessing, Advanced Models, Pharmaceuticals and Health Care.

References :

1. Navdeep Singh, Daisy Adhikari. (2023). AI in Inventory Management: Applications, Challenges, and Opportunities. https://www.researchgate.net/ publication/376032757_AI_in_Inventory_Management_Applications_Challenges_and_Opportunities
2. Dhaliwal, N., Tomar, P. K., Joshi, A., Reddy, G. S., Hussein, A., & Alazzam, M. (2023). A detailed Analysis of the Use of AI in Inventory Management for technically better management. https://ieeexplore.ieee.org/document/10183082
3. Naik, G. R. (2023). AI-Based Inventory Management System Using Odoo. https://ijsrem.com/download/ai-based-inventory-management-system-using-odoo/
4. Eldred, M., Thatcher, J., Rehman, A., Gee, I., & Suboyin, A. (2023). Leveraging AI for Inventory Management and Accurate Forecast – An Industrial Field Study. https://onepetro.org/SPEIOGS/ proceedings-abstract/22AIS/1-22AIS/D011S001 R001/515672
5. Ünal, Ö. A., Erkayman, B., & Usanmaz, B. (2023). Applications of Artificial Intelligence in Inventory Management: A Systematic Review of Literature. https://link.springer.com/article/10.1007/s11831-022-09879-5
6. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553):436-444.
7. Shrestha, A., & Mahmood, A. (2019). Review of Deep Learning Algorithms and Architectures. IEEE Access, 7, 53040–53065. doi:10.1109/access.2019.2912200
8. Lit Jens, G. et al. A Survey on Deep Learning in Medical Image Analysis. 42, 60–88 (2017).
9. Ker, J., Wang, L., Rao, J. & Lim, T. Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389. https://doi.org/10.1109/access. 2017.2788044 (2018).
10. Bharani Kumar Depuru., Sreekanth Putsala., Pragya Mishra., Automating poultry farm management with artificial intelligence: Real-time detection and tracking of broiler chickens for enhanced and efficient health monitoring. Tropical Animal Health and Production (2024) 56:75. https://doi.org/10.1007/s11250-024-03922-2
11. Mamdouh, N.; Khattab, A. YOLO-Based Deep Learning Framework for Olive Fruit Fly Detection and Counting. IEEE Access 2021, 9, 84252–84262. Available online: https://ieeexplore.ieee.org/ abstract/document/9450822 (accessed on 27 November 2021).
12. Dirir, A.; Ignatious, H.; Elsayed, H.; Khan, M.; Adib, M.; Mahmoud, A.; Al-Gunaid, M. An Efficient Multi-Object Tracking and Counting System Using Deep Learning in Urban Vehicular Environments. Future Internet 2021, 13, 306. https://doi.org/10.3390/ fi13120306
13. Moon, J.; Lim, S.; Lee, H.; Yu, S.; Lee, K.-B. Smart Counting System Based on Object Detection Using Deep Learning. Remote Sens. 2022, 14, 3761. https://doi.org/ 10.3390/rs14153761
14. X. Xu, M. Zhao, P. Shi, R. Ren, X. He, X. Wei, H. Yang, Crack detection and comparison study based on faster R-CNN and Mask R-CNN, Sensors 22 (2022) 1215, https://doi.org/ 10.3390/s22031215.
15. J. Li, D. Zhang, J. Zhang, J. Zhang, T. Li, Y. Xia, Q. Yan, L. Xun, Facial expression recognition with faster R-CNN, Proc Comput Sci 107 (2017) 135e140, https://doi.org/10.1016/ j.procs.2017.03.069.
16. H. Nguyen, Improving faster R-CNN framework for Fast vehicle detection, Math Probl Eng (2019) 11, https://doi.org/ 10.1155/2019/3808064, 2019.
17. M. Yuan, Q. Zhang, Y. Li, Y. Yan, Y. Zhu, A suspicious multi object detection and recognition method for millimeter wave SAR security inspection images based on multi-path
18. B. Leibe, J. Matas, N. Sebe, M. Welling, Computer Vision e ECCV, first ed., Springer International Publishing, Cham. (2016) https://doi.org/10.1007/ 978-3-319-46448-0
19. M. Maktab, M. Razaak, P. Remagnino, Enhanced single shot small object detector for aerial imagery using super-resolution feature fusion and deconvolution, Sensors 22 (2022) 4339, https://doi.org/10.3390/s22124339
20. G. Jocher, A. Chaurasia, J. Qiu, YOLO by Ultralytics, 2023, https://doi.org/10.5281/zenodo.3908559. https://github.com/ ultralytics/ultralytics. (accessed August 21 2023).
21. Talib, Moahaimen; Al-Noori, Ahmed H. Y.; and Suad, Jameelah (2024) "YOLOv8-CAB: Improved YOLOv8 for Real-time object detection," Karbala International Journal of Modern Science: Vol. 10: Iss. 1, Article 5. Available at: https://doi.org/ 10.33640/2405-609X.3339
22. A. Kumar Suhane, A. Vani, and U. Raghuwanshi, “HUMAN DETECTION AND CROWD COUNTING USING YOLO.” [Online]. Available: https://www.researchgate.net/publication/370341591
23. H. Gomes, N. Redinha, N. Lavado, and M. Mendes, “Counting People and Bicycles in Real Time Using YOLO on Jetson Nano,” Energies (Basel), vol. 15, no. 23, Dec. 2022, doi: 10.3390/en15238816.
24. Dan Benhamou, Mia Weiss, Matthias Borms, Julia Lucaci, Haymen Girgis, Cecile Frolet, Wesley T. Baisley, Gio Shoushi, Kristen A. Cribbs, and Manuel Wenk “Assessing the Clinical, Economic, and Health Resource Utilization Impacts of Prefilled Syringes Versus Conventional Medication Administration Methods: Results from a Systematic Literature Review https://journals.sagepub.com/doi/full/ 10.1177/10600280231212890