AN IMAGE DATASET FOR MILITARY VEHICLE DETECTION AND CLASSIFICATION (DMVDC)

The technology of intelligent detection of military vehicle objects has already become the foundation for tasks related to intelligence and tracking of weapons and equipment, which is essential for situational awareness in modern intelligent warfare. Over the past two decades, numerous datasets of military vehicle images have been collected, primarily focusing on the classification of various categories of military vehicles. However, almost all these datasets are not publicly available, and the publicly available ones suffer from annotation quality issues. To address the lack of datasets and the quality of existing public datasets, we propose a specialized dataset based on our own collection of images, as well as publicly available ones. To develop methods for automatic detection of various categories of military vehicles and distinguishing them from civilian vehicles, we created a new dataset for military vehicle detection and classification (DMVDC). It consists of 5,899 images of military vehicles collected using three different methods: automated scraping, manual selection of image search results, and data augmentation techniques. To the best of our knowledge, our DMVDC [1] dataset is the only publicly available dataset of military vehicles that consider hidden and partially visible objects. This dataset will contribute to the development of computer vision models for detecting military vehicles.

Authors

Institutions

Publication Details

Journal
Herald of Kazakh-British technical university
Published
2025-03-23
DOI
https://doi.org/10.55452/1998-6688-2025-22-1-74-83
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AN IMAGE DATASET FOR MILITARY VEHICLE DETECTION AND CLASSIFICATION (DMVDC)

Assel Abdildayeva, Talgat Islamgozhayev, M. Skakov
Herald of Kazakh-British technical university
Advanced Neural Network Applications
article

AN IMAGE DATASET FOR MILITARY VEHICLE DETECTION AND CLASSIFICATION (DMVDC)

Assel Abdildayeva, Talgat Islamgozhayev, M. Skakov
article en

Abstract

The technology of intelligent detection of military vehicle objects has already become the foundation for tasks related to intelligence and tracking of weapons and equipment, which is essential for situational awareness in modern intelligent warfare. Over the past two decades, numerous datasets of military vehicle images have been collected, primarily focusing on the classification of various categories of military vehicles. However, almost all these datasets are not publicly available, and the publicly available ones suffer from annotation quality issues. To address the lack of datasets and the quality of existing public datasets, we propose a specialized dataset based on our own collection of images, as well as publicly available ones. To develop methods for automatic detection of various categories of military vehicles and distinguishing them from civilian vehicles, we created a new dataset for military vehicle detection and classification (DMVDC). It consists of 5,899 images of military vehicles collected using three different methods: automated scraping, manual selection of image search results, and data augmentation techniques. To the best of our knowledge, our DMVDC [1] dataset is the only publicly available dataset of military vehicles that consider hidden and partially visible objects. This dataset will contribute to the development of computer vision models for detecting military vehicles.

Herald of Kazakh-British technical universityVol. 22(1)
Al-Farabi Kazakh National University (KZ), Astana Medical University (KZ)
Openalex Percentile: Top 95%
Advanced Neural Network Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.