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fegp-yolov8

This project uses the self-developed lightweight model, FEGP-YOLOv8, to achieve real-time UAV target detection. Both the accuracy and real-time performance have been significantly improved.

https://github.com/huwenmanong/fegp-yolov8

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
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    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.2%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

This project uses the self-developed lightweight model, FEGP-YOLOv8, to achieve real-time UAV target detection. Both the accuracy and real-time performance have been significantly improved.

Basic Info
  • Host: GitHub
  • Owner: huwenmanong
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 5.62 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 8 months ago · Last pushed 7 months ago
Metadata Files
Readme License Citation

README.md

FEGP-YOLOv8

This project is directly related to the manuscript in The Visual Computer: "Enhanced Feature Learning and Model Lightweighting for Real-Time UAV Detection Using FEGP-YOLOv8". We kindly request that when using this project, you cite the aforementioned manuscript from the journal.This project uses the self-developed lightweight model, FEGP-YOLOv8, to achieve real-time UAV target detection. Both the accuracy and real-time performance have been significantly improved.The usage of this project is the same as that of the official YOLOv8, so no redundant description will be given here. The datasets and algorithms used in this project are all described in detail in the manuscript. However, to avoid conflicts of interest, We are unable to provide the datasets and models developed by other teams or individuals. Please contact the original authors for access to the original datasets and models. This may cause inconvenience to readers, and we apologize for this. If readers need assistance, they can send us an email, and we will do our best to provide help within the scope of our responsibilities.

Owner

  • Login: huwenmanong
  • Kind: user

Citation (CITATION.cff) 

# This CITATION.cff file was generated with https://bit.ly/cffinit

cff-version: 1.2.0
title: Ultralytics YOLO
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Glenn
    family-names: Jocher
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0001-5950-6979'
  - given-names: Ayush
    family-names: Chaurasia
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0002-7603-6750'
  - family-names: Qiu
    given-names: Jing
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0003-3783-7069'
repository-code: 'https://github.com/ultralytics/ultralytics'
url: 'https://ultralytics.com'
license: AGPL-3.0
version: 8.0.0
date-released: '2023-01-10'

GitHub Events

Total
  • Push event: 3
  • Create event: 2
Last Year
  • Push event: 3
  • Create event: 2

Dependencies

requirements.txt pypi
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.23.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • pillow >=10.3.0
  • psutil *
  • requests >=2.32.2
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=70.0.0
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.66.3