Publication Date: 2024/02/17
Abstract: The inspection of pharmaceutical products, especially pills, has become an essential process in the pharmaceutical industry to ensure the quality and safety of medication. The traditional inspection methods are time-consuming and prone to errors. The use of deep learning models such as YOLOv5 has shown promising results in detecting and classifying pills accurately. YOLOv5 is a state-of-the-art object detection model that provides faster and more efficient processing of images with high accuracy rates. In this abstract, we review the recent studies on pill inspection using YOLOv5. We discuss the key features of YOLOv5 that make it suitable for pill inspection and the challenges associated with this task. We also highlight the potential benefits of using YOLOv5 for pill inspection, including improved accuracy, speed, and cost-effectiveness. Overall, the application of YOLOv5 in pill inspection can help ensure the authenticity and integrity of pharmaceutical products, providing a valuable tool for quality control in the pharmaceutical industry. The MAP @ 0.5 obtained was 1 after 25-27 epochs.
Keywords: Defect Detection; Micro-cracks; Photovoltaics; Smart Manufacturing; Quality Inspection.
DOI: https://doi.org/10.5281/zenodo.10673266
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24FEB687.pdf
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