---
title: "ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU…"
description: "5 papers in the tracked journals link to the Zenodo dataset ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference in their Crossref records, the first in 2022; most of them (1) in 2026."
url: https://scholar4ai.com/code/zenodo-6222936
markdown_url: https://scholar4ai.com/code/zenodo-6222936.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to the Zenodo dataset ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference in their Crossref records, the first in 2022; most of them (1) in… (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: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference: papers linking to it

Research papers in the 480 tracked journals that link to ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference (https://doi.org/10.5281/zenodo.6222936), 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
- 2022
- archives github.com/ultralytics/yolov5
- https://doi.org/10.5281/zenodo.6222936

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

## Key findings

- 5 papers in the tracked journals link to the Zenodo dataset ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference in their Crossref records, the first in 2022; most of them (1) in 2026.
- By category: AI (5); the journals with the most are Expert Systems with Applications (3), Neural Networks (1) and Applied Intelligence (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference.
- It is a Zenodo archive of ultralytics/yolov5, which other papers here link to as well.

## By year

*Papers linking to ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference by publication year*

| Year | Papers |
|---|---:|
| 2022 | 1 |
| 2023 | 1 |
| 2024 | 1 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 3 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection](https://doi.org/10.1007/s10489-022-04299-1) (Applied Intelligence, 2022)
  - [Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC)](https://doi.org/10.1016/j.eswa.2023.119741) (Expert Systems with Applications, 2023) — cites the paper above

## Related repositories

*Linked repositories ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference derives from or that derive from it*

| Repository | Relation | Papers |
|---|---|---:|
| [ultralytics/yolov5](https://scholar4ai.com/code/github-ultralytics-yolov5.md) | ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference is its Zenodo archive | 110 |

## Papers

*Papers linking to ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-02-26 | [Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC)](https://doi.org/10.1016/j.eswa.2023.119741) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Ahmet Karaman, İshak Paçal, Alper Baştürk et al. | 120 | cites it |
| 2022-11-23 | [Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection](https://doi.org/10.1007/s10489-022-04299-1) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Ahmet Karaman, Derviş Karaboğa, İshak Paçal et al. | 108 | cites it |
| 2025-05-17 | [Deep learning methods for autonomous driving scene understanding tasks: A review](https://doi.org/10.1016/j.eswa.2025.128098) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Mehwish Awan, Taeg Keun Whangbo, Jitae Shin | 12 | cites it |
| 2024-03-20 | [Revolutionizing flame detection: Novelization in flame detection through transferring distillation for knowledge to pruned model](https://doi.org/10.1016/j.eswa.2024.123787) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Hongkang Tao, Jiansheng Liu, Zan Yang et al. | 11 | cites it |
| 2026-04-18 | [Cross-modal transformer fusion via local sampling for drone RGB-infrared object detection](https://doi.org/10.1016/j.neunet.2026.108997) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | He Qi, Xuanyu Xiang, Hui Qin et al. | 2 | 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: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-6222936

```bibtex
@misc{scholar4ai-code-zenodo-6222936,
  author = {{Scholar4AI}},
  title = {ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-6222936}},
  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/).