CNN-Based Automatic Tablet Classification Using a Vibration-Controlled Bowl Feeder with Spiral Torque Optimization

TL;DR

This paper introduces a system that uses CNN and a vibration-controlled bowl feeder to automatically classify 102 types of pills with 88.8% accuracy.

Contribution

The novel integration of CNN with a vibration-controlled bowl feeder for pill classification is presented.

Findings

The CNN achieved 88.8% classification accuracy using 4080 pill images.

Optimized bowl feeder parameters enabled stable and sequential pill movement.

The system tolerates oblique angles up to 75° for precise pill alignment.

Abstract

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.

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