Ship Detection from Aerial Images

Summary #

Ship Detection from Aerial Images is a dataset for an object detection task. Possible applications of the dataset could be in the logistics and shipping industries.

The dataset consists of 621 images with 1951 labeled objects belonging to 1 single class (boat).

Images in the Ship Detection from Aerial Images 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 2020.

Explore #

Ship Detection from Aerial Images dataset has 621 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 Ship Detection from Aerial Images

Sample image from Ship Detection from Aerial Images

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Have a look at 621 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
boat➔ rectangle	621	1951	3.14	5.02%

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
boat rectangle	1951	1.62%	58.99%	0%	2px	0.54%	449px	84.4%	36px	9.97%	2px	0.5%	397px	69.89%

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 1951 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 1951

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➔	boat rectangle	boat468.png	336 x 358	37px	11.01%	85px	23.74%	2.61%
2➔	boat rectangle	boat577.png	420 x 468	129px	30.71%	104px	22.22%	6.83%
3➔	boat rectangle	boat76.png	462 x 520	146px	31.6%	132px	25.38%	8.02%
4➔	boat rectangle	boat675.png	354 x 362	56px	15.82%	108px	29.83%	4.72%
5➔	boat rectangle	boat500.png	498 x 478	237px	47.59%	66px	13.81%	6.57%
6➔	boat rectangle	boat419.png	490 x 470	128px	26.12%	116px	24.68%	6.45%
7➔	boat rectangle	boat349.png	270 x 272	66px	24.44%	29px	10.66%	2.61%
8➔	boat rectangle	boat148.png	384 x 400	207px	53.91%	177px	44.25%	23.85%
9➔	boat rectangle	boat361.png	364 x 500	112px	30.77%	124px	24.8%	7.63%
10➔	boat rectangle	boat361.png	364 x 500	128px	35.16%	146px	29.2%	10.27%

License #

Ship Detection from Aerial Images is under CC0 1.0 license.

Source

Citation #

If you make use of the Ship Detection from Aerial Images data, please cite the following reference:

@dataset{Ship Detection from Aerial Images,
	author={Andrew Maranhão},
	title={Ship Detection from Aerial Images},
	year={2020},
	url={https://www.kaggle.com/datasets/andrewmvd/ship-detection}
}

Source

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

@misc{ visualization-tools-for-aerial-ship-detection-dataset,
  title = { Visualization Tools for Ship Detection from Aerial Images Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/aerial-ship-detection } },
  url = { https://datasetninja.com/aerial-ship-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset Ship Detection from Aerial Images 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='Ship Detection from Aerial Images', 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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