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YOLO-Pi: Real-Time Object Recognition on Raspberry Pi

YOLO-Pi brings powerful real-time object detection capabilities to the Raspberry Pi platform by combining YOLO (You Only Look Once) neural networks with efficient edge computing.

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What is YOLO-Pi?

YOLO-Pi is a computer vision project that enables automatic object detection through a USB camera attached to a Raspberry Pi. The system processes live video feeds in real-time and identifies objects using pre-trained YOLO models converted to Keras format. The project captures video from a camera, processes each frame through a YOLO neural network, and publishes detection results via MQTT for downstream applications like home automation, security monitoring, or IoT analytics.

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Technology Stack

YOLO-Pi leverages a carefully selected set of technologies optimized for embedded machine learning:

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Core Framework

  • YOLO (You Only Look Once): State-of-the-art object detection algorithm that processes images in a single pass
  • Keras: High-level neural network API for model inference
  • TensorFlow: Backend engine for neural network computation
  • YAD2K: Converter tool that transforms YOLO weights and configurations into Keras models

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Computer Vision

  • OpenCV 3: Real-time computer vision library for camera capture and image processing
  • Pillow: Image manipulation for preprocessing and annotation
  • NumPy: Efficient array operations for image data

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Communication

  • MQTT (Paho): Lightweight messaging protocol for publishing detection events
  • JSON: Structured data format for detection results

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Hardware Requirements

YOLO-Pi is designed to run on resource-constrained hardware:
Minimum Hardware
  • Raspberry Pi 3 or newer (Raspberry Pi 3+ recommended)
  • USB camera compatible with Video4Linux (v4l)
  • 2GB+ swap space for compilation
  • MicroSD card (16GB+ recommended)
Compiling dependencies on Raspberry Pi takes considerable time (20+ minutes for Docker builds). The project includes pre-built Docker images to simplify deployment.

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Supported Models

YOLO-Pi supports multiple pre-trained YOLO models with different accuracy and speed tradeoffs: Optimized for speed on embedded devices, this model achieves approximately 1 frame every 2 seconds on a MacBook Pro, with lower frame rates on Raspberry Pi. 
model_path = 'model_data/tiny-yolo-voc.h5'
anchors_path = 'model_data/tiny-yolo-voc_anchors.txt'
classes_path = 'model_data/pascal_classes.txt'

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Full YOLO COCO

Higher accuracy model trained on the COCO dataset with 80 object classes. 
model_path = 'model_data/yolo.h5'
anchors_path = 'model_data/yolo_anchors.txt'
classes_path = 'model_data/coco_classes.txt'

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Detection Classes

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Pascal VOC (20 classes)

The tiny-yolo-voc model detects 20 common object categories:
  • Vehicles: aeroplane, bicycle, boat, bus, car, motorbike, train
  • Animals: bird, cat, cow, dog, horse, sheep
  • Furniture: chair, diningtable, sofa, tvmonitor
  • Objects: bottle, person, pottedplant

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COCO Dataset (80 classes)

The full YOLO model supports a broader range of objects including sports equipment, food items, electronics, and more.

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Key Features

  • Real-time Detection: Continuous video processing with live object identification
  • MQTT Integration: Publishes detection events with object class and confidence scores
  • Bounding Box Visualization: Annotates detected objects with colored boxes and labels
  • Flexible Model Selection: Easy switching between accuracy and speed tradeoffs
  • Docker Support: Containerized deployment for both x86 and ARM architectures
  • Headless Operation: Runs without display for server deployments

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Detection Output

YOLO-Pi generates structured JSON data for each detection: 
[
  {"item": "person", "score": "0.87"},
  {"item": "chair", "score": "0.62"}
]
This data is published to the yolo MQTT topic with timestamps for event correlation.

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Architecture Overview

The system architecture follows this pipeline:
1

Video Capture

OpenCV captures frames from the USB camera at /dev/video0
2

Preprocessing

Images are converted from BGR to RGB, resized to model input dimensions, and normalized
3

Inference

The Keras model processes the image through the YOLO network to detect objects
4

Post-processing

Bounding boxes are filtered by confidence threshold (0.3) and non-maximum suppression (IOU 0.5)
5

Output

Detection results are published via MQTT and optionally visualized with bounding boxes

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Use Cases

  • Home Security: Detect and alert on specific objects or people in camera view
  • Wildlife Monitoring: Identify animals in outdoor camera feeds
  • Inventory Management: Track objects entering/leaving a space
  • Smart Home Automation: Trigger actions based on detected objects
  • Educational Projects: Learn computer vision and edge AI deployment
YOLO-Pi prioritizes inference speed over training. The project uses pre-trained weights from the official YOLO project and focuses on efficient deployment rather than model training.

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Next Steps

Ready to get started? Check out the Quickstart Guide to set up YOLO-Pi and run your first object detection.

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