Real-Time Object Detection Web Application using YOLOv8 with image upload and live webcam detection
- Upload images → Get object detection with bounding boxes
- Start webcam → Real-time object detection
- Detects 80 different object classes
- YOLOv8 - Latest AI model for object detection
- Flask - Python web framework
- OpenCV - Computer vision processing
- PyTorch - Deep learning framework
- Real-time at ~30 FPS
- 80 object classes (person, car, bicycle, etc.)
- High accuracy with state-of-the-art model
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Show Interface (10 sec)
- "This is our web-based object detection app"
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Upload Image (1 min)
- Upload test image
- Show results: "Here we detected a person with 85% confidence"
- Explain bounding boxes and labels
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Live Detection (1-2 min)
- Click "Start Live Detection"
- Point webcam at objects
- "See how it detects in real-time!"
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Technical Overview (30 sec)
- "Uses YOLOv8, one of the best object detection models"
- "Flask backend processes everything"
- "OpenCV handles image/video processing"
Q: Why YOLOv8?
A: Latest version, faster and more accurate than YOLOv3, supports 80 object classes.
Q: How fast is it?
A: Real-time performance at ~30 frames per second.
Q: What objects can it detect?
A: 80 different objects including person, car, bicycle, laptop, phone, bottle, etc.
Q: Can it be used in production?
A: Yes, Flask server can be deployed, and we can use larger YOLOv8 models for better accuracy.
Q: What are the applications?
A: Security surveillance, traffic monitoring, retail analytics, autonomous vehicles, etc.
# Start server
cd "real time object detection"
source .venv/bin/activate
python app.py
# Then open browser
http://127.0.0.1:8080- Test image upload works
- Webcam works
- Server starts without errors
- Browser opens the app
- Have sample images ready
- Webcam permissions enabled
You've got this! 🎯