Scholar4AI Updated 2026-11-03 14:43 UTC+09:00

ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements: papers linking to it

Zenodo · Software · 2020 · archives github.com/ultralytics/yolov5 · doi.org/10.5281/zenodo.4154370

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements (https://doi.org/10.5281/zenodo.4154370), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

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Key findings#

  • 11 papers in the tracked journals link to the Zenodo dataset ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements in their Crossref records, the first in 2021; most of them (4) in 2025.
  • By category: AI (10) and Operations Research (1); the journals with the most are Expert Systems with Applications (4), Neural Computing and Applications (3) and International Journal of Computer Vision (2).
  • It is a Zenodo archive of ultralytics/yolov5, which other papers here link to as well.

By year#

2021: 1 paper2022: 3 papers2023: 2 papers2025: 4 papers2026: 1 paper
20212026
Papers linking to ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements by publication year
YearPapers
20211
20223
20232
20254
20261

Journals#

Journals whose papers link to ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements most
JournalPapers
Expert Systems with Applications4
Neural Computing and Applications3
International Journal of Computer Vision2
Computers & Industrial Engineering1
Neural Networks1
Linked repositories ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements derives from or that derive from it
RepositoryRelationPapers
ultralytics/yolov5ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements is its Zenodo archive110
Papers linking to ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements, most cited first
PublishedTitleJournalAuthorsCitationsLink
2022-08-24Artificial Intelligence for Dunhuang Cultural Heritage Protection: The Project and the DatasetInternational Journal of Computer VisionTianxiu Yu, Cong Lin, Shijie Zhang et al.103cites it
2022-06-14Similarity based person re-identification for multi-object tracking using deep Siamese networkNeural Computing and ApplicationsHarun Suljagic, Ertuğrul Bayraktar, Numan Çelebi54cites it
2021-06-20Personal Protection Equipment detection system for embedded devices based on DNN and Fuzzy LogicExpert Systems with ApplicationsGiancarlo Iannizzotto, Lucia Lo Bello, Gaetano Patti48cites it
2023-05-08Location and timestamp-based chip contour detection using LWMG-YOLOv5Computers & Industrial EngineeringBao Rong Chang, Hsiu Fen Tsai, Chia-Wei Hsieh17cites it
2025-04-03NU-AIR: A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and VehiclesInternational Journal of Computer VisionCraig Iaboni, Thomas Kelly, Pramod Abichandani9cites it
2023-09-14QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditionsNeural Computing and ApplicationsFatimah Islam, Muhib Ullah, Abdul Bais6cites it
2025-07-08Uncertainty-guided attention learning for malaria parasite detection in thick blood smearsNeural NetworksHao Xiong, Zhiyong Wang, Roneel V. Sharan et al.4cites it
2025-08-24Region-aware metric learning for few-shot detection of counterfeit cigarettes from packaging imagesExpert Systems with ApplicationsQian Zhou, Huanrou Ding, Chengzhe Li et al.4cites it
2025-12-29A low-cost temporal-spatial calibration method for radar and camera systemsExpert Systems with ApplicationsWei He, Gui Zhang, Xinyu Liu et al.3cites it
2022-09-01LRATD: a lightweight real-time abnormal trajectory detection approach for road traffic surveillanceNeural Computing and ApplicationsChun Zhang, Keyan Ren2cites it
2026-06-13Two-stage acoustic event detection and classification for horn signals in urban scenarios: Comparing VGGish, YAMNet and Mel spectrogram approaches with mRMR feature selectionExpert Systems with ApplicationsVahid Hajihashemi, Abdorreza Alavi Gharahbagh, José J. M. Machado et al.0cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-4154370

@misc{scholar4ai-code-zenodo-4154370,
  author = {{Scholar4AI}},
  title = {ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-4154370}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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