The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification
Open accessAbstract
Background: The 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. Methods: In 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. Results: The 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. Conclusion: Our 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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Cited by
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
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YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO
Hussain, M. (2024) · IEEE Access · doi:10.1109/access.2024.3378568 · Cited by 492
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
Ragab, M. G., Abdulkadir, S. J., Muneer, A., Alqushaibi, A., Sumiea, E. H., Qureshi, R., Al-Selwi, S. M., & Alhussian, H. (2024) · IEEE Access · doi:10.1109/access.2024.3386826 · Cited by 306
Mahasin, M., & Dewi, I. A. (2022) · International Journal of Engineering, Science and Information Technology · doi:10.52088/ijesty.v2i3.291 · Cited by 71
Metadata
- Authors
- Tan, L., Huangfu, T., Wu, L., & Chen, W.
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2021-11-22
- Volume
- 21
- Issue
- 1
- Pages
- 324-324
- ISSN
- 1472-6947 (ISSN-L)
- OpenAlex
- W3215073785
- Cited by
- 224
- References
- 2
- FWCI
- 12.52
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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