Quick Start Guide: Raspberry Pi and Pi Camera with YOLOv5 and YOLOv8

This comprehensive guide aims to expedite your journey with YOLO object detection models on a Raspberry Pi using a Pi Camera. Whether you're a student, hobbyist, or a professional, this guide is designed to get you up and running in less than 30 minutes. The instructions here are rigorously tested to minimize setup issues, allowing you to focus on utilizing YOLO for your specific projects.

Watch: Raspberry Pi 5 updates and improvements.

Prerequisites

• Raspberry Pi 3, 4 or 5
• Pi Camera
• 64-bit Raspberry Pi Operating System

Connect the Pi Camera to your Raspberry Pi via a CSI cable and install the 64-bit Raspberry Pi Operating System. Verify your camera with the following command:

libcamera-hello

You should see a video feed from your camera.

Choose Your YOLO Version: YOLOv5 or YOLOv8

This guide offers you the flexibility to start with either YOLOv5 or YOLOv8. Both versions have their unique advantages and use-cases. The choice is yours, but remember, the guide's aim is not just quick setup but also a robust foundation for your future work in object detection.

Hardware Specifics: At a Glance

To assist you in making an informed hardware decision, we've summarized the key hardware specifics of Raspberry Pi 3, 4, and 5 in the table below:

Please make sure to follow the instructions specific to your Raspberry Pi model to ensure a smooth setup process.

Quick Start with YOLOv5

This section outlines how to set up YOLOv5 on a Raspberry Pi with a Pi Camera. These steps are designed to be compatible with the libcamera camera stack introduced in Raspberry Pi OS Bullseye.

Install Necessary Packages

1. Update the Raspberry Pi:

   sudo apt-get update
   sudo apt-get upgrade -y
   sudo apt-get autoremove -y
   

2. Clone the YOLOv5 repository:

   cd ~
   git clone https://github.com/Ultralytics/yolov5.git
   

3. Install the required dependencies:

   cd ~/yolov5
   pip3 install -r requirements.txt
   

4. For Raspberry Pi 3, install compatible versions of PyTorch and Torchvision (skip for Raspberry Pi 4):

   pip3 uninstall torch torchvision
   pip3 install torch==1.11.0 torchvision==0.12.0
   

Modify detect.py

To enable TCP streams via SSH or the CLI, minor modifications are needed in detect.py.

1. Open detect.py:

   sudo nano ~/yolov5/detect.py
   

2. Find and modify the is_url line to accept TCP streams:

   is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://', 'tcp://'))
   

3. Comment out the view_img line:

   # view_img = check_imshow(warn=True)
   

4. Save and exit:

   CTRL + O -> ENTER -> CTRL + X
   

Initiate TCP Stream with Libcamera

1. Start the TCP stream:

   libcamera-vid -n -t 0 --width 1280 --height 960 --framerate 1 --inline --listen -o tcp://127.0.0.1:8888
   

Keep this terminal session running for the next steps.

Perform YOLOv5 Inference

1. Run the YOLOv5 detection:

   cd ~/yolov5
   python3 detect.py --source=tcp://127.0.0.1:8888
   

Quick Start with YOLOv8

Follow this section if you are interested in setting up YOLOv8 instead. The steps are quite similar but are tailored for YOLOv8's specific needs.

Install Necessary Packages

1. Update the Raspberry Pi:

   sudo apt-get update
   sudo apt-get upgrade -y
   sudo apt-get autoremove -y
   

2. Install the ultralytics Python package:

   pip3 install ultralytics
   

3. Reboot:

   sudo reboot
   

Initiate TCP Stream with Libcamera

1. Start the TCP stream:

   libcamera-vid -n -t 0 --width 1280 --height 960 --framerate 1 --inline --listen -o tcp://127.0.0.1:8888
   

Perform YOLOv8 Inference

To perform inference with YOLOv8, you can use the following Python code snippet:

from ultralytics import YOLO

model = YOLO('yolov8n.pt')
results = model('tcp://127.0.0.1:8888', stream=True)

while True:
    for result in results:
        boxes = result.boxes
        probs = result.probs

Next Steps

Congratulations on successfully setting up YOLO on your Raspberry Pi! For further learning and support, visit Ultralytics and Kashmir World Foundation.

Acknowledgements and Citations

This guide was initially created by Daan Eeltink for Kashmir World Foundation, an organization dedicated to the use of YOLO for the conservation of endangered species. We acknowledge their pioneering work and educational focus in the realm of object detection technologies.

For more information about Kashmir World Foundation's activities, you can visit their website.

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