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x-cdnet

https://github.com/xgyutu/x-cdnet

Science Score: 57.0%

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
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    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (4.8%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: xgyutu
  • Language: Python
  • Default Branch: main
  • Size: 1.07 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme Citation

README.md

X-CDNet: A real-time crosswalk detector based on YOLOX

Authors

  • Xingyuan Lu
  • Yanbing Xue
  • Zhigang Wang
  • Haixia Xu
  • Xianbin Wen

Paper

Abstract

As urban traffic safety becomes increasingly important, real-time crosswalk detection is playing a critical role in the transportation field. However, existing crosswalk detection algorithms must be improved in terms of accuracy and speed. This study proposes a real-time crosswalk detector called X-CDNet based on YOLOX. Based on the ConvNeXt basic module, we designed a new basic module called Reparameterizable Sparse Large-Kernel (RepSLK) convolution that can be used to expand the model’s receptive field without the addition of extra inference time. In addition, we created a new crosswalk dataset called CD9K, which is based on realistic driving scenes augmented by techniques such as synthetic rain and fog. The experimental results demonstrate that X-CDNet outperforms YOLOX in terms of both detection accuracy and speed. X-CDNet achieves a 93.3 AP50 and a real-time detection speed of 123 FPS.

Figure 1

Figure 1

Figure 2

Figure 2

Owner

  • Login: xgyutu
  • 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'

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