LADD: Lacmus Drone Dataset

Introduction #

Mikhail Shuranov, Denis Shurenkov, Dmitry Ruzhitskyet al.

The LADD: Lacmus Drone Dataset dataset was developed by non-profit search and rescue volunteer organizations with a focus on locating missing individuals, collecting the data using drones, and employing machine learning tools for labeling. These images were captured from an altitude of 40-50 meters above the ground, and they portray individuals in different poses.

The authors engaged in extensive discussions with various search engines and rescuers in an attempt to comprehend the visual characteristics of a person lost in a forest when viewed from an aerial perspective. Consequently, they accumulated unique statistics pertaining to 24 common positions where missing individuals are typically located. The initial version of the dataset comprises more than 400 images. The data collection process primarily utilized DJI Mavic Pro and Phantom drones, operating at altitudes ranging from 50 to 100 meters, with image resolutions set at 3000x4000 pixels and an average human-size representation of 50x100 pixels.

In total, the dataset comprises 1365 images, and the annotations for LADD are accessible in VOC format, specifically using Xmax, Ymax, Xmin, Ymin coordinates, as well as in YOLO format, using XYWH values.

Please, note that the dataset is constantly updating, and this version is relevant as of October, 2023.

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Summary #

LADD: Lacmus Drone Dataset is a dataset for an object detection task. It is used in the search and rescue (SAR) industry.

The dataset consists of 1365 images with 4733 labeled objects belonging to 1 single class (pedestrian).

Images in the LADD: Lacmus Drone Dataset dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2023 by the Lacmus Project, Russia.

Explore #

LADD: Lacmus Drone Dataset dataset has 1365 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from LADD: Lacmus Drone Dataset

Sample image from LADD: Lacmus Drone Dataset

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Have a look at 1365 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
pedestrian➔ rectangle	1365	4733	3.47	0.13%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-1 of 1

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
pedestrian rectangle	4733	0.04%	0.18%	0%	10px	0.33%	187px	6.23%	69px	2.24%	14px	0.35%	191px	4.78%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 4733 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 4733

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	pedestrian rectangle	1349.jpg	3000 x 4000	47px	1.57%	61px	1.52%	0.02%
2➔	pedestrian rectangle	1349.jpg	3000 x 4000	34px	1.13%	46px	1.15%	0.01%
3➔	pedestrian rectangle	1349.jpg	3000 x 4000	40px	1.33%	38px	0.95%	0.01%
4➔	pedestrian rectangle	1349.jpg	3000 x 4000	33px	1.1%	50px	1.25%	0.01%
5➔	pedestrian rectangle	1349.jpg	3000 x 4000	61px	2.03%	64px	1.6%	0.03%
6➔	pedestrian rectangle	1349.jpg	3000 x 4000	53px	1.77%	39px	0.97%	0.02%
7➔	pedestrian rectangle	647.jpg	3078 x 5472	61px	1.98%	153px	2.8%	0.06%
8➔	pedestrian rectangle	220.jpg	2250 x 4000	58px	2.58%	81px	2.02%	0.05%
9➔	pedestrian rectangle	123.jpg	3000 x 4000	58px	1.93%	142px	3.55%	0.07%
10➔	pedestrian rectangle	123.jpg	3000 x 4000	71px	2.37%	156px	3.9%	0.09%

License #

LADD: Lacmus Drone Dataset is under GNU GPL 3.0 license.

Citation #

If you make use of the LADD data, please cite the following reference:

@dataset{LADD,
  author={Mikhail Shuranov and Denis Shurenkov and Dmitry Ruzhitsky and Victoria Martynova and Ekaterina Bykova and Georgy Perevozchikov},
  title={LADD: Lacmus Drone Dataset},
  year={2023},
  url={https://www.kaggle.com/datasets/mersico/lacmus-drone-dataset-ladd-v40}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-lacmus-drone-dataset-dataset,
  title = { Visualization Tools for LADD: Lacmus Drone Dataset Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/lacmus-drone-dataset } },
  url = { https://datasetninja.com/lacmus-drone-dataset },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset LADD: Lacmus Drone Dataset can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='LADD: Lacmus Drone Dataset', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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