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Safety Helmet Detection Dataset

500033376
Tagsafety, surveillance
Taskobject detection
Release YearMade in 2020
LicenseCC0 1.0
Download1 GB

Summary #

Dataset LinkHomepage

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.

Dataset Poster

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.

OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
OpenSample annotation mask from Safety Helmet DetectionSample image from Safety Helmet Detection
👀
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.

Search
Rows 1-3 of 3
Class
Images
Objects
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 25502
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
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 = { jul },
  note = { visited on 2024-07-25 },
}

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.

. . .

Disclaimer #

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