Introduction #
Authors introduce OD-WeaponDetection: Knife Classification dataset, a collection of 10 039 images designed for classification task, contains 100 classes: aaaknife, airplanes, barrel and more. These images were sourced from the internet, including frames extracted from YouTube videos and surveillance footage. The dataset encompasses a wide variety of cold steel weapons, differing in types, shapes, colors, sizes, and materials. It accounts for knives positioned at various distances from the camera, some partially occluded by hands, and objects that mimic the handling of knives. The dataset offers a diverse range of indoor and outdoor scenarios, and additional details about this image dataset and experiment results can be found in the related publication. The OD-WeaponDetection: Knife Classification dataset is a part of Weapon Detection Open Data.
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.
- OD-WeaponDetection: Knife Classification (10 039 images, 100 classes) (current)
- OD-WeaponDetection: Pistol Classification (9 857 images, 102 classes) (available on DatasetNinja)
- OD-WeaponDetection: Sohas Classification (9 544 images, 6 classes) (available on DatasetNinja)
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.
- OD-WeaponDetection: Knife Detection (2 078 images, 1 class) (available on DatasetNinja)
- OD-WeaponDetection: Pistol Detection (3 000 images, 1 class) (available on DatasetNinja)
- OD-WeaponDetection: Sohas Detection (5 859 images, 6 classes) (available on DatasetNinja)
Expand
Homepage
Research Paper
Blog PostSummary #
OD-WeaponDetection: Knife Classification is a dataset for a classification task. It is used in the security industry.
The dataset consists of 10039 images with 0 labeled objects. There are no pre-defined train/val/test splits in the dataset. The dataset designed for classification task, contains 100 classes: aaaknife, airplanes, barrel and more. The dataset was released in 2020 by the University of Granada, Spain.
Here are the visualized examples for the classes:
Explore #
OD-WeaponDetection: Knife Classification dataset has 10039 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.
License #
OD-WeaponDetection: Knife Classification is under CC BY-SA 4.0 license.
Citation #
If you make use of the OD-WeaponDetection: Knife Classification data, please cite the following reference:
@article{CASTILLO2019151,
title = {Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning},
journal = {Neurocomputing},
volume = {330},
pages = {151-161},
year = {2019},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2018.10.076},
url = {https://www.sciencedirect.com/science/article/pii/S0925231218313365},
author = {Alberto Castillo and Siham Tabik and Francisco Pérez and Roberto Olmos and Francisco Herrera},
}
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-knife-classification-dataset,
title = { Visualization Tools for OD-WeaponDetection: Knife Classification Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/od-weapon-detection-knife-classification } },
url = { https://datasetninja.com/od-weapon-detection-knife-classification },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { nov },
note = { visited on 2024-11-21 },
}Download #
Dataset OD-WeaponDetection: Knife Classification 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: Knife Classification', 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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