نظام ذكي لاكتشاف غطاء وختم لقاحات باستخدام الذكاء الاصطناعي

AI-Based Vaccine Cap Seal Detection System

An intelligent real-time vaccine cap and seal detection system built using YOLO and Supervision. It automatically verifies whether vaccine vials are properly sealed, providing visual indicators, tracking counts, and status annotations to ensure quality assurance in pharmaceutical packaging lines.

Project Description

The Vaccine Cap Detection System leverages YOLO-based object detection and Supervision annotation tools to perform automated quality inspection in vaccine production environments. It monitors live or recorded video feeds of vials on conveyor belts and classifies each vial as sealed or unsealed.

• ✅ Cap Present (OK) — properly sealed vial
• ❌ Cap Missing (Not OK) — defective or unsealed vial

Core Objectives

• 💉 Detect vaccine vials and cap presence.
• 🎥 Enable real-time inspection via video or live feed.
• 🧠 Use YOLO deep learning models for accurate classification.
• 🔁 Track multiple vials with persistent IDs.
• 🧾 Display clear visual status annotations.
• 🧩 Support dual inspection zones (Before / After).

Key Features

• ✅ Real-time vial and cap detection.
• ✅ Dual-zone inspection analysis.
• ✅ OK / Not OK icon visualization.
• ✅ Supervision annotators (box, line, icon, label).
• ✅ ByteTrack for consistent object tracking.
• ✅ Configurable via config.yaml.
• ✅ Lightweight and industrial-grade performance.

System Workflow

1. Input video feed from conveyor cameras.
2. YOLO detects vials and cap presence.
3. ByteTrack assigns unique IDs.
4. Line zones trigger before/after inspection.
5. Supervision annotates status and icons.
6. Annotated output displayed in real time.

Technologies Used

• 🧠 Python 3.x
• 🧠 Ultralytics YOLO
• 🧠 Supervision
• 🧠 ByteTrack
• 🧠 OpenCV
• 🧠 config.yaml configuration

Use Cases

• 🧪 Pharmaceutical Manufacturing
• 🏭 Production Line Automation
• 🧾 Quality Assurance
• 🧠 AI-Based Defect Detection
• 🔬 Industrial AI Research

Possible Enhancements

• ☁️ Real-time dashboards and logging.
• 📊 Automated defect reporting.
• 🧠 Detection of additional anomalies.
• 🛰️ Edge deployment (Jetson, Raspberry Pi).
• 🔊 Audio / visual alerts.

Conclusion

The Vaccine Cap Detection System using YOLO and Supervision automates pharmaceutical quality control by verifying cap seals with high accuracy. It reduces human error, improves efficiency, and ensures reliable vaccine packaging through AI-powered visual inspection.