---
title: "ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models…"
description: "4 papers in the tracked journals link to the Zenodo dataset ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support in their Crossref records, the first in 2022; most of them (2) in 2023."
url: https://scholar4ai.com/code/zenodo-5563715
markdown_url: https://scholar4ai.com/code/zenodo-5563715.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the Zenodo dataset ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support in their Crossref records, the first in 2022… (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support: papers linking to it

Research papers in the 480 tracked journals that link to ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support (https://doi.org/10.5281/zenodo.5563715), 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.

- Zenodo
- Software
- 2021
- archives github.com/ultralytics/yolov5
- https://doi.org/10.5281/zenodo.5563715

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the Zenodo dataset ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support in their Crossref records, the first in 2022; most of them (2) in 2023.
- By category: AI (4); the journals with the most are Neural Computing and Applications (3) and Expert Systems with Applications (1).
- It is a Zenodo archive of ultralytics/yolov5, which other papers here link to as well.

## By year

*Papers linking to ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support by publication year*

| Year | Papers |
|---|---:|
| 2022 | 2 |
| 2023 | 2 |

## Journals

*Journals whose papers link to ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 3 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |

## Related repositories

*Linked repositories ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support derives from or that derive from it*

| Repository | Relation | Papers |
|---|---|---:|
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support is its Zenodo archive | 110 |

## Papers

*Papers linking to ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2022-03-01 | [Remote sensing image super-resolution and object detection: Benchmark and state of the art](https://doi.org/10.1016/j.eswa.2022.116793) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Yi Wang, Syed Muhammad Arsalan Bashir, Mahrukh Khan et al. | 244 | cites it |
| 2023-02-06 | [A hybrid method for fire detection based on spatial and temporal patterns](https://doi.org/10.1007/s00521-023-08260-2) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Pedro Vinícius Almeida Borges de Venâncio, Roger Júnio Campos, Tamires Martins Rezende et al. | 53 | cites it |
| 2022-09-02 | [Automatic detection of indoor occupancy based on improved YOLOv5 model](https://doi.org/10.1007/s00521-022-07730-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Chao Wang, Yunchu Zhang, Yanfei Zhou et al. | 34 | cites it |
| 2023-04-18 | [A video system based on convolutional autoencoder for drowning detection](https://doi.org/10.1007/s00521-023-08526-9) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Xinyu He, Fei Yuan, Tingzhuang Liu et al. | 10 | cites 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: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-5563715

```bibtex
@misc{scholar4ai-code-zenodo-5563715,
  author = {{Scholar4AI}},
  title = {ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, Roboflow integration, TensorFlow export, OpenCV DNN support: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-5563715}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).