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t1_object_detection

https://github.com/uom-msc-robotics-2024-team-1/t1_object_detection

Science Score: 44.0%

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    Low similarity (12.0%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: UoM-MSc-Robotics-2024-Team-1
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 20.4 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

Leo Rover Object Detection with YOLOv5

This project enhances the Leo Rover's environmental interaction by using the lightweight YOLOv5 model for object detection, focusing on recognizing the shape, position, and color of objects. And object info can be published to ROS2 topic.\ Please make sure ROS2 is ready for using on your device.

Features

  • Shape Recognition: Classify basic shapes to aid in navigation and interaction.
  • Position Detection: Determine the precise location of objects for mapping and obstacle avoidance.
  • Color Recognition: Identify colors of objects for specific tasks like sorting or analysis.
  • ROS2 Topic Publish: Publish object info to ROS2 topic.

Getting Started

Clone the repository to get started with enhancing your Leo Rover:

bash git clone https://github.com/UoM-MSc-Robotics-2024-Team-1/t1_object_detection.git

Prerequisites

Ensure you have Python 3.8+ installed on your system:

bash python --version

Installation

Install the necessary Python packages:

bash pip install -r requirements.txt bash pip install pyrealsense2

Usage

Usage To run the detection script, use the following command:

python python detect.py The script supports several optional arguments:

--weights: Set the model path or triton URL. Default is pre-configured to a specific path. Change this to where your model's weights are stored：

python python detect.py --weights path/to/your/model_weights.pt

--source: Define the source of the input. It can be a file path, directory, URL, glob pattern, or a camera ID ('0' for webcam). The default is 6, adjust as needed：

python python detect.py --source yourimage.jpg

--data: Specify the dataset configuration file path (dataset.yaml). The default path is set; update it according to your dataset or configuration file's location：

python python detect.py --data path/to/your/dataset.yaml Ensure to modify these arguments according to your setup and the requirements of your detection task.

Owner

  • Name: UoM MSc Robotics 2024: Team 1
  • Login: UoM-MSc-Robotics-2024-Team-1
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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Dependencies

utils/docker/Dockerfile docker
  • pytorch/pytorch 2.0.0-cuda11.7-cudnn8-runtime build
utils/google_app_engine/Dockerfile docker
  • gcr.io/google-appengine/python latest build
requirements.txt pypi
  • Pillow >=9.4.0
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.23.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232
utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==2.3.2
  • gunicorn ==19.10.0
  • pip ==23.3
  • werkzeug >=3.0.1
pyproject.toml pypi
  • matplotlib >=3.3.0
  • numpy >=1.22.2
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • thop >=0.1.1
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232