VisionGuard AI

Real-time computer vision system for automated safety compliance monitoring.

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Features

• Real-time object detection using YOLO
• Intelligent tracking and association
• Multi-source video processing (webcam, IP camera, files)
• Annotated video output

Tech Stack

• AI/ML: YOLOv8, YOLOv12
• Vision: OpenCV
• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript

Installation

git clone <your-repo-url>
cd visionguard-ai

python3 -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt

Usage

Local Monitoring

python3 workplace_safety_monitor.py --source 0 --ppe-weights best.pt

Web Interface

python3 start_localhost.py

• Frontend: http://localhost:8000
• Backend: http://localhost:5001

Cloud Demo (Vercel)

• URL: https://ppe-safety-detection-ai.vercel.app
• Note: Runs in Client-Side Demo Mode using TensorFlow.js (simulated safety checks) as typical serverless environments do not support GPU-heavy YOLO models.

Configuration

• --conf-helmet: Detection threshold (default: 0.65)
• --conf-vest: Detection threshold (default: 0.70)
• --track-iou: Tracking consistency

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Personal AI Project | 2026