Safety Helmet and Reflective Jacket

Introduction #

Nirav B Naik

The Safety Helmet and Reflective Jacket dataset contains 10,500 images that have been annotated with bounding boxes for two vital object classes: safety_helmet and reflective_jacket. The main objective behind this dataset is to facilitate the training of an object detection model using the YOLOv7 architecture to accurately identify and locate safety equipment within a diverse array of settings and environments. To ensure effective model development and evaluation, the dataset has been divided into train, test, and val subsets, maintaining a balanced ratio of 70% for training and 15% each for testing and validation, resulting in a comprehensive 100% split.

The dataset includes images captured from diverse locations, such as construction sites, factories, and outdoor work environments. Each image contains an average of five annotated objects, providing a comprehensive set of scenarios for the model to learn from.

The Safety Helmet and Reflective Jacket dataset is particularly useful for industries that require workers to wear safety helmets and reflective jackets, such as construction, manufacturing, and mining. By training a model on this dataset, companies can automate safety monitoring and reduce the risk of accidents caused by the lack of appropriate safety equipment.

The dataset is provided in the YOLOv7 format and includes annotations for each image in a text file. The images are available in JPEG format, making it easy to use with different deep-learning frameworks. The dataset’s large size and the diversity of images and objects make it a valuable resource for training a robust and accurate object detection model.

Expand

Homepage

Summary #

Safety Helmet and Reflective Jacket is a dataset for an object detection task. Possible applications of the dataset could be in the safety industry.

The dataset consists of 10500 images with 36240 labeled objects belonging to 2 different classes including reflective_jacket and safety_helmet.

Images in the Safety Helmet and Reflective Jacket dataset have bounding box annotations. There are 49 (0% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (7350 images), test (1575 images), and valid (1575 images). The dataset was released in 2023.

Explore #

Safety Helmet and Reflective Jacket dataset has 10500 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 and Reflective Jacket

Sample image from Safety Helmet and Reflective Jacket

👀

Have a look at 10500 images

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

Class balance #

There are 2 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 1-2 of 2

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
reflective_jacket➔ rectangle	8803	16049	1.82	24.13%
safety_helmet➔ rectangle	8570	20191	2.36	7.29%

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 1-2 of 2

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
safety_helmet rectangle	20191	3.13%	95.83%	0.01%	8px	1.25%	629px	98.28%	95px	14.87%	7px	1.09%	631px	98.59%
reflective_jacket rectangle	16049	13.45%	100%	0.01%	11px	1.72%	640px	100%	247px	38.62%	3px	0.47%	640px	100%

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 36240 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 36240

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➔	reflective_jacket rectangle	helmet_jacket_07375.jpg	640 x 640	440px	68.75%	426px	66.56%	45.76%
2➔	reflective_jacket rectangle	helmet_jacket_07876.jpg	640 x 640	349px	54.53%	202px	31.56%	17.21%
3➔	safety_helmet rectangle	helmet_jacket_07876.jpg	640 x 640	199px	31.09%	101px	15.78%	4.91%
4➔	safety_helmet rectangle	helmet_jacket_07876.jpg	640 x 640	116px	18.12%	91px	14.22%	2.58%
5➔	reflective_jacket rectangle	helmet_jacket_07876.jpg	640 x 640	298px	46.56%	154px	24.06%	11.2%
6➔	reflective_jacket rectangle	helmet_jacket_07738.jpg	640 x 640	353px	55.16%	103px	16.09%	8.88%
7➔	reflective_jacket rectangle	helmet_jacket_07738.jpg	640 x 640	329px	51.41%	88px	13.75%	7.07%
8➔	safety_helmet rectangle	helmet_jacket_07738.jpg	640 x 640	96px	15%	64px	10%	1.5%
9➔	safety_helmet rectangle	helmet_jacket_07738.jpg	640 x 640	91px	14.22%	61px	9.53%	1.36%
10➔	safety_helmet rectangle	helmet_jacket_07738.jpg	640 x 640	145px	22.66%	74px	11.56%	2.62%

License #

Safety Helmet and Reflective Jacket is under Apache 2.0 license.

Source

Citation #

If you make use of the Safety Helmet and Reflective Jacket data, please cite the following reference:

@dataset{Safety Helmet and Reflective Jacket,
  author={Nirav B Naik},
  title={Safety Helmet and Reflective Jacket},
  year={2023},
  url={https://www.kaggle.com/datasets/niravnaik/safety-helmet-and-reflective-jacket}
}

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-and-reflective-jacket-dataset,
  title = { Visualization Tools for Safety Helmet and Reflective Jacket Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/safety-helmet-and-reflective-jacket } },
  url = { https://datasetninja.com/safety-helmet-and-reflective-jacket },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-01 },
}

Download #

Dataset Safety Helmet and Reflective Jacket 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 and Reflective Jacket', 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 #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.