Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification
| العنوان: | Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification |
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| المؤلفون: | Tianran Huangfu, Lu Tan, Wenying Chen, Liyao Wu |
| المصدر: | BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021) BMC Medical Informatics and Decision Making |
| بيانات النشر: | BMC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, YOLO v3, Computer applications to medicine. Medical informatics, R858-859.7, Health Informatics, Sample (statistics), Convolutional neural network, RetinaNet, Humans, SSD, business.industry, Research, Health Policy, Detector, Single shot, Pattern recognition, Frame rate, Silver Sulfadiazine, Object detection, Computer Science Applications, Identification (information), Pill, Neural Networks, Computer, Artificial intelligence, business, Pill identification, Algorithms |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 1472-6947 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bbbab34ca2277f7eb436a1bcc8cf33b https://doaj.org/article/b8730b5e504e4900ae76aa43042b7a96 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....4bbbab34ca2277f7eb436a1bcc8cf33b |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 14726947 |
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