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OD-WeaponDetection: Sohas Detection Dataset

585961
Tagsecurity, surveillance
Taskobject detection
Release YearMade in 2020
LicenseCC BY-SA 4.0
Download2 GB

Introduction #

Released 2020-11-23 ·Francisco Pérez-Hernández, Siham Tabik, Alberto Lamaset al.

Authors introduce OD-WeaponDetection: Sohas Detection dataset, featuring a corpus of 5859 images meticulously labeled with 6446 objects spanning six distinct classes, which include knive, pistol, tarjeta, monedero, smartphone, billete categories. This dataset is strategically divided into two key segments: a train split comprising 5002 images and a test split with 857 images. Just as in the example dataset, the authors present this resource as a pivotal contribution to the field of object detection, particularly focusing on weapons and objects of interest within images. The dataset’s rich content is sourced from a myriad of internet-based sources, including frames extracted from YouTube videos and surveillance footage, ensuring it encapsulates real-world challenges. It boasts the inclusion of various knives, mirroring the diverse types, shapes, colors, sizes, and materials that such weapons can embody. Additionally, the dataset addresses complex scenarios where knives are partially obscured by hands and objects simulating their handling, enhancing its practicality. With a diverse array of indoor and outdoor settings, the dataset equips researchers, security professionals, and developers with a versatile resource for advancing object detection models. Detailed information and experimental results are available in a related publication, making the OD-WeaponDetection: Sohos Detection dataset an essential component of the broader Weapon Detection Open Data initiative.

More about Weapon Detection Open Data

The weapon datasets available here are specifically tailored for the development of intelligent video surveillance automatic systems.

An automatic weapon detection system can provide the early detection of potentially violent situations that is of paramount importance for citizens security. One way to prevent these situations is by detecting the presence of dangerous objects such as handguns and knives in surveillance videos. Deep Learning techniques based on Convolutional Neural Networks can be trained to detect this type of object.

The weapon detection task can be performed by different approaches of combining a region proposal technique with a classifier, or integrating both into one model. However, any deep learning model requires to learn a quality image dataset and an annotation according to the classification or detection tasks.

Weapon detection Open Data provides quality image datasets built for training Deep Learning models under the development of an automatic weapon detection system. Weapons datasets for image classification and object detection tasks are described and can be downloaded below. The public datasets are organized depending on the included objects in the dataset images and the target task.

Weapon Detection Open Data structure

Classification

The datasets included in this section have been designed for the classification task based on CNN deep learning models. After the training stage on these datasets, the classification models must distinguish between weapons and different common objects present in the background or handled similarly.

Detection

The datasets included in this section have been designed for the object detection task based on Deep Learning architectures with a CNN backbone. The selected images contain weapons and objects but also consider an enriched context of different background objects as well as the way objects are handled. After the training stage on these datasets, the detection models must locate and distinguish between weapons and different common objects present in the background or handled similarly.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkBlog Post

Summary #

OD-WeaponDetection: Sohas Detection is a dataset for an object detection task. It is used in the security industry.

The dataset consists of 5859 images with 6446 labeled objects belonging to 6 different classes including knife, pistol, smartphone, and other: monedero, billete, and tarjeta.

Images in the OD-WeaponDetection: Sohas Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (5002 images) and test (857 images). The dataset was released in 2020 by the University of Granada, Spain, Ho Chi Minh City University of Technology (HUTECH), Viet Nam, and King Abdulaziz University (KAU) Jeddah, Saudi Arabia.

Here are the visualized examples for the classes:

Explore #

OD-WeaponDetection: Sohas Detection dataset has 5859 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 OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
OpenSample annotation mask from OD-WeaponDetection: Sohas DetectionSample image from OD-WeaponDetection: Sohas Detection
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Have a look at 5859 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 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 1-6 of 6
Class
Images
Objects
Count on image
average
Area on image
average
knife
rectangle
2277
2349
1.03
9.96%
pistol
rectangle
1510
1663
1.1
45.31%
smartphone
rectangle
715
893
1.25
29.72%
monedero
rectangle
611
644
1.05
27.37%
billete
rectangle
482
584
1.21
25.53%
tarjeta
rectangle
279
313
1.12
21.31%

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 1-6 of 6
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
knife
rectangle
2349
9.68%
88.35%
0.17%
23px
2.26%
2820px
99.66%
196px
29.65%
26px
2.34%
2744px
99.95%
pistol
rectangle
1663
41.42%
99.43%
0.31%
27px
4.91%
1401px
99.82%
254px
58.08%
45px
3.83%
1808px
99.82%
smartphone
rectangle
893
24.59%
99.18%
0.32%
52px
4.81%
1919px
99.95%
715px
62.57%
62px
3.23%
1916px
99.79%
monedero
rectangle
644
26.14%
98.44%
0.45%
24px
6.3%
1446px
99.57%
310px
43.87%
79px
4.64%
1827px
99.86%
billete
rectangle
584
21.85%
99.55%
0.36%
43px
4.72%
1311px
99.83%
288px
42.43%
72px
4.17%
1816px
99.9%
tarjeta
rectangle
313
19.41%
99.09%
0.12%
37px
3.43%
761px
99.45%
217px
39.46%
42px
2.19%
1069px
99.86%

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 6446 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 6446
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
smartphone
rectangle
smartphone_1015.jpg
1281 x 1920
454px
35.44%
395px
20.57%
7.29%
2
pistol
rectangle
pistol_5013.jpg
1281 x 1920
318px
24.82%
409px
21.3%
5.29%
3
knife
rectangle
DefenseKnifeAttack0376.jpg
720 x 1280
90px
12.5%
191px
14.92%
1.87%
4
smartphone
rectangle
smartphone_1033.jpg
1281 x 1920
258px
20.14%
196px
10.21%
2.06%
5
knife
rectangle
LBmframe00160.jpg
1090 x 1920
106px
9.72%
61px
3.18%
0.31%
6
knife
rectangle
MBsframe00334.jpg
1090 x 1920
70px
6.42%
110px
5.73%
0.37%
7
knife
rectangle
knife_64.jpg
529 x 800
384px
72.59%
634px
79.25%
57.53%
8
knife
rectangle
KravMagaKnifeDefenseTechniques062.jpg
720 x 1280
69px
9.58%
143px
11.17%
1.07%
9
smartphone
rectangle
smartphone_1071.jpg
1080 x 1920
282px
26.11%
157px
8.18%
2.14%
10
monedero
rectangle
img772.jpg
1080 x 1920
103px
9.54%
131px
6.82%
0.65%

License #

OD-WeaponDetection: Sohas Detection is under CC BY-SA 4.0 license.

Source

Citation #

If you make use of the OD-WeaponDetection: Sohas Detection data, please cite the following reference:

@article{article,
  author = {Pérez, Francisco and Tabik, Siham and Castillo Lamas, Alberto and Olmos, Roberto and Fujita, Hamido and Herrera, Francisco},
  year = {2020},
  month = {02},
  pages = {105590},
  title = {Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance},
  volume = {194},
  journal = {Knowledge-Based Systems},
  doi = {10.1016/j.knosys.2020.105590}
}

Source

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

@misc{ visualization-tools-for-od-weapon-detection-sohas-detection-dataset,
  title = { Visualization Tools for OD-WeaponDetection: Sohas Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/od-weapon-detection-sohas-detection } },
  url = { https://datasetninja.com/od-weapon-detection-sohas-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset OD-WeaponDetection: Sohas 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='OD-WeaponDetection: Sohas 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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