Object Detection for Autonomous Vehicles

This project develops an object detection system optimized for autonomous vehicles, using the YOLO architecture for real-time detection. It is trained and tested on the COCO Dataset.

Table of Contents

• Overview
• Dataset
• Installation
• Usage

Overview

Object detection is critical for the safe operation of autonomous vehicles. This system uses YOLOv8 to detect objects in real-time from video streams, focusing on optimizing accuracy and speed for autonomous driving applications.

Dataset

We use the COCO Dataset for training and validation. Ensure the download the dataset and place it in the data/coco/ folder.

Installation

1. Clone the repository:

   git clone https://github.com/hmatoui-username/object-detection-autonomous-vehicles.git
   cd object-detection-autonomous-vehicles

2. Install dependencies

   pip install -r requirements.txt

3. Set Up YOLOv8

   1. Install Ultralytics Library: YOLOv8 is available in the ultralytics package. Install it via pip:

      pip install ultralytics

   2. Import the YOLOv8 Module: In the Python scripts, use from ultralytics import YOLO to access YOLOv8's functionalities.

   3. Organize the Dataset:

      The dataset should be in the YOLO format:

      data/coco/
      ├── annotations
      ├── images/train2017       # Training images
      ├── images/val2017         # Validation images
      ├── labels/train2017       # Training labels
      └── labels/val2017         # Validation labels
      

   4. Prepare Dataset Configuration File: Create a dataset configuration file (data/coco.yaml):

      path: ../datasets/coco # Dataset root directory
      train: train2017.txt # Training images directory
      val: val2017.txt # Validation images directory
      test: test-dev2017.txt # Testing images directory
      
      names:
         0: person
         1: bicycle
         2: car
         3: motorcycle
         4: airplane
         5: bus
         6: train
         7: truck
         8: boat
         9: traffic light
         10: fire hydrant
         11: stop sign
         12: parking meter
         13: bench
         14: bird
         15: cat
         16: dog
         17: horse
         18: sheep
         19: cow
         20: elephant
         21: bear
         22: zebra
         23: giraffe
         24: backpack
         25: umbrella
         26: handbag
         27: tie
         28: suitcase
         29: frisbee
         30: skis
         31: snowboard
         32: sports ball
         33: kite
         34: baseball bat
         35: baseball glove
         36: skateboard
         37: surfboard
         38: tennis racket
         39: bottle
         40: wine glass
         41: cup
         42: fork
         43: knife
         44: spoon
         45: bowl
         46: banana
         47: apple
         48: sandwich
         49: orange
         50: broccoli
         51: carrot
         52: hot dog
         53: pizza
         54: donut
         55: cake
         56: chair
         57: couch
         58: potted plant
         59: bed
         60: dining table
         61: toilet
         62: tv
         63: laptop
         64: mouse
         65: remote
         66: keyboard
         67: cell phone
         68: microwave
         69: oven
         70: toaster
         71: sink
         72: refrigerator
         73: book
         74: clock
         75: vase
         76: scissors
         77: teddy bear
         78: hair drier
         79: toothbrush
      
      # Download script/URL (optional)
      download: |
      from ultralytics.utils.downloads import download
      from pathlib import Path
      
      # Download labels
      segments = True  # segment or box labels
      dir = Path(yaml['path'])  # dataset root dir
      url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
      urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
      download(urls, dir=dir.parent)
      # Download data
      urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
               'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
               'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
      download(urls, dir=dir / 'images', threads=3)
      

Usage

Training

Train YOLO on the COCO dataset:

python scripts/train.py --data data/coco/ --epochs 50

Detection

Run object detection on images or video streams:

python scripts/test.py