Pharmaceutical QC Defect Detection Machine
Computer vision system for detecting defects in pharmaceutical blister packs.
Full project details and source code cannot be provided because this is a private and confidential enterprise project. This project emphasizes industrial AI vision: dataset collection, defect-case design, model training, accuracy validation, inference optimization, industrial cameras, industrial PCs and edge deployment constraints.
Full project details and source code cannot be provided because this is a private and confidential enterprise project.
Project Highlights
Machine-vision inspection flow for pharmaceutical blister quality control.
Captured production sample used for defect analysis and validation.
Camera/frame sample for visual inspection and model testing.
Industrial image sample used during data review and edge validation.
Video & Walkthrough
Timeline
Behind The Project
Overview
The project focused on designing and developing a computer vision inspection system for pharmaceutical quality control. It integrated industrial camera input, image preprocessing, object detection, model inference, result classification, and reporting into a production-oriented workflow.
Problem
Manual inspection in pharmaceutical production can be time-consuming, inconsistent, and dependent on human checking. The business needed a system that could support visual inspection, detect abnormal conditions, and reduce operational dependency on manual QC workflows.
Approach
The system was designed as an end-to-end computer vision workflow for production-oriented inspection. Key components included: Industrial camera input, image preprocessing, deep learning and object detection, model inference, defect and abnormal condition classification, result reporting, and workflow optimization.
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