https://github.com/alexeyev/hogweed-ground-level-view
A dataset for semantic segmentation of Sosnowsky's hogweed in the ground-level view photos taken in St. Petersburg, Malaya Vishera, Pushkin, etc.
https://github.com/alexeyev/hogweed-ground-level-view
agtech coco-format computer-vision data dataset ecology plant-detection robotic-vision semantic-segmentation
Last synced: over 1 year ago
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A dataset for semantic segmentation of Sosnowsky's hogweed in the ground-level view photos taken in St. Petersburg, Malaya Vishera, Pushkin, etc.
- Host: GitHub
- URL: https://github.com/alexeyev/hogweed-ground-level-view
- Owner: alexeyev
- License: mit
- Created: 2021-08-22T13:29:59.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-10-03T18:20:50.000Z (over 4 years ago)
- Last Synced: 2025-01-08T16:33:25.502Z (over 1 year ago)
- Topics: agtech, coco-format, computer-vision, data, dataset, ecology, plant-detection, robotic-vision, semantic-segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 19.3 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Detecting Hogweed on the Ground-Level View Photographs: Dataset
Hogweed (Heracleum) is a herbs genus that features many invasive species such as giant hogweed
or Sosnowsky's hogweed. This invasive species are particularly notorious due to the high
content of phototoxic compounds, so that any contact with a plant may result in an intense skin burn.
Invasion of the [Sosnowsky's hogweed](https://antiborschevik.info/) \[lang:RU\] in particular is major trouble
in Central Russia, and by 2021 resolving the problem requires massive intervention. Agtech drones spraying
herbicides are already used to eradicate the Sosnowsky's hogweed, and accompanying real-time detection
algorithms for UAVs are being developed (e.g. see [this paper](https://ieeexplore.ieee.org/document/9359491)
and [the related dataset repository](https://github.com/DLopatkin/Heracleum-Dataset)).
We propose a dataset for detecting Sosnowsky's hogweed using the ground-level view as if we're
looking through the camera of an **autonomous unmanned ground vehicle** patrolling the hogweed-endangered
area (e.g. a week after mowing or poisoning). It is not 100% clear whether this dataset can or should be
used for training actual robotic vision algorithms or synthetic datasets construction. However, plant detection
in the natural environment is quite a challenge, which makes such annotated images collections suitable
for competitions and/or ML homeworks. This is a *grassroot* (pun intended) initiative without any external
funding.
## Data
Photographic images for the directory `prepared_data/images/` (CC-BY-4.0) can be **downloaded from Zenodo: [5233380](https://zenodo.org/record/5233380)**.
**444** (311/133) photos are taken in different locations in Russia using a Samsung Galaxy A31 camera.
The images are annotated using https://supervise.ly/ (CE).
A more detailed description of the data collection strategy and the dataset in general will be released during autumn.
Test set annotations will be released after the end of the competition.
## Format
The annotations are provided in COCO format. To inspect the annotations manually, please see
the Jupyter notebook `COCO-formatted-annotations-viewer.ipynb` adapted from
the [original Gist](https://gist.github.com/akTwelve/dc79fc8b9ae66828e7c7f648049bc42d)
shared by [akTwelve](https://github.com/akTwelve).
### Classification
To train a classifier,
1. run a `get_data.sh` script,
2. check out the Dataset object provided in `dataset.py` if you are planning to use PyTorch,
3. consider using a baseline implemented in `prepared_pipeline_for_transfer.py` --
based on a fine-tuned `ResNet18` model prepared by Dustin Franklin @dusty-nv. The training process
is described in the [tutorial](https://github.com/dusty-nv/jetson-inference/blob/master/docs/pytorch-plants.md).
The model is available for [downloading](https://nvidia.box.com/s/dslt9b0hqq7u71o6mzvy07w0onn0tw66). All rights
are reserved by NVIDIA.
## How to cite
We would appreciate if you cite this dataset as
```
@dataset{alekseev_anton_2021_5233380,
author = {Alekseev, Anton},
title = {{Detecting Hogweed on the Ground-Level View Photographs: Dataset}},
month = aug,
year = 2021,
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5233380},
url = {https://doi.org/10.5281/zenodo.5233380}
}
```
## Acknowledgements
I would like to thank Aleksey Artamonov, Andrey Savchenko and Mikhail Evtikhiev
for various consultations and proofreading.
## Other materials
* [A monster that devours Russia](https://www.youtube.com/watch?v=u5NxuEoXHn8) \[YouTube video\]
* Different species, similar threat: [Giant Hogweed - The UK's Most Dangerous & Toxic Plant](https://www.youtube.com/watch?v=p2iCSHrYjoc) \[YouTube video, possibly disturbing content\]
