Safety Helmet Detection - Dataset Ninja

Summary #

Safety Helmet Detection is a dataset for an object detection task. Possible applications of the dataset could be in the safety and surveillance industries.

The dataset consists of 5000 images with 25502 labeled objects belonging to 3 different classes including helmet, head, and person.

Images in the Safety Helmet Detection 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 #

Safety Helmet Detection dataset has 5000 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 Safety Helmet Detection

Sample image from Safety Helmet Detection

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Have a look at 5000 images

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

Class balance #

There are 3 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 1-3 of 3

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
helmet➔ rectangle	4581	18966	4.14	5.35%
head➔ rectangle	920	5785	6.29	4.06%
person➔ rectangle	158	751	4.75	32%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

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 1-3 of 3

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
helmet rectangle	18966	1.32%	50.92%	0.01%	2px	0.48%	334px	80.29%	42px	10.01%	5px	1.2%	283px	68.03%
head rectangle	5785	0.66%	15.35%	0.04%	5px	1.2%	207px	49.88%	33px	7.91%	5px	1.2%	145px	34.86%
person rectangle	751	7.16%	63.65%	0.08%	10px	2.41%	413px	99.28%	117px	28.09%	7px	1.68%	405px	97.36%

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 25502 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 25502

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➔	helmet rectangle	hard_hat_workers1454.png	415 x 416	78px	18.8%	76px	18.27%	3.43%
2➔	helmet rectangle	hard_hat_workers1454.png	415 x 416	91px	21.93%	78px	18.75%	4.11%
3➔	helmet rectangle	hard_hat_workers1454.png	415 x 416	130px	31.33%	127px	30.53%	9.56%
4➔	helmet rectangle	hard_hat_workers1454.png	415 x 416	51px	12.29%	77px	18.51%	2.27%
5➔	helmet rectangle	hard_hat_workers1454.png	415 x 416	36px	8.67%	79px	18.99%	1.65%
6➔	helmet rectangle	hard_hat_workers1454.png	415 x 416	60px	14.46%	128px	30.77%	4.45%
7➔	helmet rectangle	hard_hat_workers4707.png	415 x 416	35px	8.43%	29px	6.97%	0.59%
8➔	helmet rectangle	hard_hat_workers4707.png	415 x 416	33px	7.95%	31px	7.45%	0.59%
9➔	helmet rectangle	hard_hat_workers4707.png	415 x 416	31px	7.47%	29px	6.97%	0.52%
10➔	helmet rectangle	hard_hat_workers4707.png	415 x 416	35px	8.43%	29px	6.97%	0.59%

License #

Safety Helmet Detection is under CC0 1.0 license.

Source

Citation #

If you make use of the Safety Helmet Detection data, please cite the following reference:

@dataset{Safety Helmet Detection,
	author={Andrew Maranhão},
	title={Safety Helmet Detection},
	year={2020},
	url={https://www.kaggle.com/datasets/andrewmvd/hard-hat-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-safety-helmet-detection-dataset,
  title = { Visualization Tools for Safety Helmet Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/safety-helmet-detection } },
  url = { https://datasetninja.com/safety-helmet-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-01 },
}

Download #

Dataset Safety Helmet Detection 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='Safety Helmet Detection', 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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Disclaimer #

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