Yoon Kicheol, Lee Sangyun, Park Junha, Kim Kwang Gi
Gachon Biomedical Convergence Institute, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea.
Department of Radiological Science, Dongnam Health University, Suwon 16328, Republic of Korea.
Sensors (Basel). 2025 Jul 8;25(14):4248. doi: 10.3390/s25144248.
This paper proposes a drug classification system using convolutional neural network (CNN) training and rotational pill dropping technology. Images of 40 pills for each of 102 types (total 4080 images) were captured, achieving a CNN classification accuracy of 88.8%. The system uses a bowl feeder with optimized operating parameters-voltage, torque, PWM, tilt angle, vibration amplitude (0.2-1.5 mm), and frequency (4-40 Hz)-to ensure stable, sequential pill movement without loss or clumping. Performance tests were conducted at 5 V, 20 rpm, 20% PWM (@40 Hz), and 1.5 mm vibration amplitude. The bowl feeder structure tolerates oblique angles up to 75°, enabling precise pill alignment and classification. The CNN model plays a key role in accurate pill detection and classification.
本文提出了一种使用卷积神经网络（CNN）训练和旋转药丸掉落技术的药物分类系统。采集了102种类型中每种40粒药丸的图像（共4080张图像），CNN分类准确率达到88.8%。该系统使用具有优化操作参数（电压、扭矩、脉宽调制、倾斜角度、振动幅度（0.2 - 1.5毫米）和频率（4 - 40赫兹））的碗式进料器，以确保药丸稳定、有序移动，无损失或结块。在5伏、20转/分钟、20%脉宽调制（@40赫兹）和1.5毫米振动幅度下进行了性能测试。碗式进料器结构可耐受高达75°的倾斜角度，实现药丸的精确对齐和分类。CNN模型在药丸的准确检测和分类中起着关键作用。
