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ROAD-SEC Dataset

1322111
Tagsecurity, surveillance
Taskobject detection
Release YearMade in 2023
LicenseDbCL v1.0
Download8 GB

Introduction #

Hector Acosta

ROAD-SEC stands as an object detection dataset comprising 13221 images, featuring annotations belonging to a singular class - road_element. Captured by stationary security cameras, these images offer diverse perspectives of highways and roads. This dataset encompasses a wide array of road elements, including but not limited to cars, trucks, pedestrians, and various environmental conditions such as differing weather patterns and lighting scenarios. The diversity in elements and environmental variations within the dataset provides a rich resource for training models to detect and understand the complexities of road elements within varying conditions and situations, crucial for applications in traffic analysis, road security, and surveillance systems.

The YOLO annotated dataset of highways and roads is a collection of images and corresponding annotation files that have been labeled using the You Only Look Once (YOLO) algorithm. The images in this dataset were captured by fixed security cameras and show different views of highways and roads. The dataset includes a variety of different elements, such as cars, trucks, and pedestrians, as well as different weather and lighting conditions. The annotations in the dataset provide detailed information about the objects in the images, including their location, size, and class.

The dataset is designed to be used for training and testing computer vision algorithms and models, particularly those focused on object detection and classification in the context of highway and road security. The annotations in the dataset were carefully curated by human annotators, ensuring high-quality labeling and accurate object recognition. This dataset can be a valuable resource for researchers, developers, and engineers who are working on computer vision projects related to highway and road security, as well as those interested in the YOLO algorithm and object detection techniques.

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Dataset LinkHomepage

Summary #

ROAD-SEC is a dataset for an object detection task. Possible applications of the dataset could be in the security and surveillance industries.

The dataset consists of 13221 images with 166192 labeled objects belonging to 1 single class (road_element).

Images in the ROAD-SEC dataset have bounding box annotations. There are 164 (1% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (9265 images), val (2926 images), and test (1030 images). The dataset was released in 2023.

Dataset Poster

Explore #

ROAD-SEC dataset has 13221 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 ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
OpenSample annotation mask from ROAD-SECSample image from ROAD-SEC
πŸ‘€
Have a look at 13221 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.

Search
Rows 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
road_elementβž”
rectangle
13057
166192
12.73
6.89%

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 1-1 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
road_element
rectangle
166192
0.59%
18.52%
0.04%
11px
1.83%
412px
68.67%
39px
6.54%
8px
1.33%
230px
38.33%

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 100157 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 100157
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
road_element
rectangle
cam_18_137.png
600 x 600
44px
7.33%
27px
4.5%
0.33%
2βž”
road_element
rectangle
cam_18_137.png
600 x 600
41px
6.83%
29px
4.83%
0.33%
3βž”
road_element
rectangle
cam_18_137.png
600 x 600
80px
13.33%
52px
8.67%
1.16%
4βž”
road_element
rectangle
cam_18_137.png
600 x 600
135px
22.5%
89px
14.83%
3.34%
5βž”
road_element
rectangle
cam_18_137.png
600 x 600
56px
9.33%
41px
6.83%
0.64%
6βž”
road_element
rectangle
cam_18_137.png
600 x 600
80px
13.33%
52px
8.67%
1.16%
7βž”
road_element
rectangle
cam_18_137.png
600 x 600
155px
25.83%
102px
17%
4.39%
8βž”
road_element
rectangle
cam_18_137.png
600 x 600
27px
4.5%
29px
4.83%
0.22%
9βž”
road_element
rectangle
cam_18_137.png
600 x 600
53px
8.83%
21px
3.5%
0.31%
10βž”
road_element
rectangle
cam_18_137.png
600 x 600
54px
9%
42px
7%
0.63%

License #

ROAD-SEC is under DbCL v1.0 license.

Source

Citation #

If you make use of the ROAD-SEC data, please cite the following reference:

@dataset{ROAD-SEC,
  author={Hector Acosta},
  title={ROAD-SEC},
  year={2023},
  url={https://www.kaggle.com/datasets/hectorandac/road-sec}
}

Source

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

@misc{ visualization-tools-for-road-sec-dataset,
  title = { Visualization Tools for ROAD-SEC Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/road-sec } },
  url = { https://datasetninja.com/road-sec },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset ROAD-SEC 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='ROAD-SEC', 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.

. . .

Disclaimer #

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