ArticleBMC medical informatics and decision making2021
Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 208 citations in OpenAlex.
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- A lightweight hybrid deep learning framework for multi-pill detection, multi-attribute recognition, OCR-based imprint analysis, and metadata retrieval.Frontiers in artificial intelligence · 2026Article
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- Using deep learning model integration to build a smart railway traffic safety monitoring system.Scientific reports · 2025Article
- Automated Image Clarity Detection for the Improvement of Colposcopy Imaging with Multiple Devices.Biomedical signal processing and control · 2025Article
- Enhancing UAV object detection with an efficient multi-scale feature fusion framework.PloS one · 2025Article
- Leveraging object detection for early diagnosis of neurodegenerative diseases through radiomic analysis.Frontiers in aging neuroscience · 2025Article
- OS-DETR: End-to-end brain tumor detection framework based on orthogonal channel shuffle networks.PloS one · 2025Article
- An AI-powered framework for assessing teacher performance in classroom interactions: a deep learning approach.Frontiers in artificial intelligence · 2025Article
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Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe correct identification of pills is very important to ensure the safe administration of drugs to patients. Here, we use three current mainstream object detection models, namely RetinaNet, Single Shot Multi-Box Detector (SSD), and You Only Look Once v3(YOLO v3), to identify pills and compare the associated performance.
methodsIn this paper, we introduce the basic principles of three object detection models. We trained each algorithm on a pill image dataset and analyzed the performance of the three models to determine the best pill recognition model. The models were then used to detect difficult samples and we compared the results.
resultsThe mean average precision (MAP) of RetinaNet reached 82.89%, but the frames per second (FPS) is only one third of YOLO v3, which makes it difficult to achieve real-time performance. SSD does not perform as well on the indicators of MAP and FPS. Although the MAP of YOLO v3 is slightly lower than the others (80.69%), it has a significant advantage in terms of detection speed. YOLO v3 also performed better when tasked with hard sample detection, and therefore the model is more suitable for deployment in hospital equipment.
conclusionOur study reveals that object detection can be applied for real-time pill identification in a hospital pharmacy, and YOLO v3 exhibits an advantage in detection speed while maintaining a satisfactory MAP.
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