PHARMACEUTICAL INSPECTION SYSTEM
AISHWARYA LAKSHMI M , KAVIPRIYA S , LAVANYA K , SRI SWETHA B A
Department of Artificial Intelligence and Machine Learning
Sri Shakthi Institute of Engineering and Technology, Coimbatore, India
ABSTRACT
Pharmaceutical quality assurance is a critical aspect of the healthcare industry, requiring exceptional precision and efficiency to ensure the safety, efficacy, and compliance of pharmaceutical products. The increasing complexity of manufacturing processes and stringent regulatory standards pose significant challenges to traditional inspection methods, which rely heavily on manual labor. These methods are not only time-consuming but also prone to human error, which can compromise product quality and safety. To address these challenges, this project introduces a revolutionary system for pharmaceutical inspection powered by YOLOv11, the latest advancement in object detection algorithms. YOLOv11 (You Only Look Once, Version 11) is renowned for its unparalleled accuracy and real-time processing capabilities. This system automates critical quality assurance tasks, including the identification of pharmaceutical products, detection of physical defects, verification of labeling accuracy, and identification of counterfeit medicines. The proposed system integrates advanced image
preprocessing techniques with YOLOv11's state-of-the-art feature extraction and detection capabilities. By leveraging deep learning, the system efficiently identifies even the smallest inconsistencies. The application of YOLOv11 in pharmaceutical inspection offers a smarter, faster, and more reliable alternative to traditional methods. Its innovative approach to object detection not only streamlines operations but also sets new benchmarks for accuracy and compliance in quality assurance. This project paves the way for AI-driven inspection workflows, addressing critical industry challenges and ensuring the highest standards of product safety and efficacy.
Keywords—YOLOv11, pharmaceutical inspection, object detection, quality assurance, counterfeit detection.
