Building a Computer Vision Assisted Pill Inspection System

A system designed to automatically detect damaged pills, foreign matter, and incorrect pills during the manufacturing process aims to enhance quality assurance and prevent potential health hazards, recalls, and reputation damage. The system utilizes a computer vision model trained on a cloud server, which is deployed on a controller linked to a conveyor belt, enabling high-speed and accurate visual inspections of pills. The process involves collecting and preparing a pill dataset, training the model using Roboflow Train, and deploying it via API to edge devices like Raspberry Pi. The model, trained with high accuracy, is capable of distinguishing between correct and damaged pills, facilitating efficient sorting and packaging. The architecture is adaptable for various manufacturing settings, providing intelligent augmentation for quality assurance processes.

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