Today’s the day your Raspberry Pi truly comes to life—because now, it can see the world. In this lesson, we connect the Pi Camera Module 2, fire up real-time object detection, and see firsthand how the AI HAT+ supercharges live vision processing.
🎯 What You’ll Learn Today:
- How to physically connect and test the Pi Camera Module 2
- How to run live object detection using a preloaded model
- How the AI HAT+ dramatically boosts frame rate (FPS) in real-time AI tasks
- How to visualize detection results with bounding boxes and confidence scores
📷 Camera Setup Steps:
- Power off your Pi
- Connect the ribbon cable to the CSI port (contacts facing HDMI side)
- Power the Pi back on
- Run libcamera-hello to confirm the camera works
🤖 Live Object Detection Tasks:
- Load a MobileNet or YOLOv5 model
- Use OpenCV to access the camera feed
- Draw bounding boxes and display confidence scores
- Optionally overlay FPS for real-time performance feedback
⚡ See the Speed Difference (AI HAT+ vs No HAT):
- Without AI HAT+: ~2–5 FPS
- With AI HAT+: ~10–20+ FPS
- That’s 2–5× faster—perfect for robotics, automation, and edge AI
🧪 Today’s Activity:
- Run the demo detection script
- Watch what your Pi recognizes in your environment
- Try holding up different objects or walking into the frame
- Challenge: Find the quirkiest object it correctly identifies!
🛠️ Troubleshooting Checklist:
- Double-check the ribbon cable orientation and connection
- Ensure libcamera is installed and working
- Confirm your AI model loads successfully and matches your runtime (ONNX or TFLite)
📝 Homework:
- Customize the detection script (new model? tweak thresholds?)
- Capture a photo or video of your Pi detecting objects
- Share your results in the #ai-camera thread on Discord
🚀 Up Next:
Now that your Pi can see, next lesson we’ll teach it to do something about it—like turning on lights or sending alerts when someone enters the room. We’re bringing vision and action together.