Drug quality re-inspection based on YOLO deep learning

Jinghan Cheng

doi:10.1117/12.2641004

13 October 2022 Drug quality re-inspection based on YOLO deep learning

Jinghan Cheng

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871R (2022) https://doi.org/10.1117/12.2641004
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

The drug quality re-inspector is an application of machine vision artificial intelligence technology to drug quality reinspection. In this paper, the author proposes a lightweight deep convolutional neural network machine learning model using a custom-trained YOLO machine learning algorithm to detect the integrity of pharmaceuticals. More importantly, it uses artificial neural networks (ANNs) to model complex relationships in the data. A color drug image segmentation method using a combination of HSV model and color distance is used to address the case of uniformly colored drug attachment in drug images. After testing, the accuracy rate was significantly improved compared to the traditional manual rechecking, eventually reaching 86%.

Citation Download Citation

Jinghan Cheng "Drug quality re-inspection based on YOLO deep learning", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871R (13 October 2022); https://doi.org/10.1117/12.2641004

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KEYWORDS

Image processing

Image acquisition

Machine vision

Tablets

Inspection

Detection and tracking algorithms

Visual process modeling

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