Detail publikace

Utilizing a CNN for Automatic Detection of Military Reconnaissance and Surveillance Objects in Aerial Images: Concept and Challenges

LIGOCKI, A. GÁBRLÍK, P. ŽALUD, L. MICHENKA, K.

Originální název

Utilizing a CNN for Automatic Detection of Military Reconnaissance and Surveillance Objects in Aerial Images: Concept and Challenges

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The utilization of compact unmanned aircraft systems (UAS) for military reconnaissance and surveillance is experiencing growth in the intelligence branch. Obtaining a large amounts of data by these means leads to the need for their quick and efficient processing for further use within the commander’s decision-making process (battle management). This paper focuses on the automatic detection of military reconnaissance and surveillance objects, such as vehicles or soldiers, in aerial images by employing the YOLOv8 object detector, a convolutional neural network (CNN) model. To achieve a high detection success rate across diverse military equipment, weather conditions, and geographic locations, a comprehensive dataset comprising thousands of images is essential for training the neural network. However, publicly available datasets of this nature are scarce, presenting a significant challenge. This study utilizes a custom image set with more than ten thousand annotated objects, whereas the data were collected from internet databases, social networks, and military training the authors participated in. The data enabled us to examine distinct preprocessing approaches and model training setups to deliver beneficial findings for future research directions. The model performance results indicate a promising detection success rate according to standard evaluation metrics; however, to ensure the algorithm’s robustness for practical applications, a considerably larger amount of data will be required.

Klíčová slova

Object detection; CNN; YOLOv8; Military objects; UAS; Aerial images

Autoři

LIGOCKI, A.; GÁBRLÍK, P.; ŽALUD, L.; MICHENKA, K.

Vydáno

25. 10. 2024

Nakladatel

Springer

Místo

Cham

ISBN

978-3-031-71396-5

Kniha

Lecture Notes in Computer Science

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Ročník

14615

Stát

Spolková republika Německo

Strany od

335

Strany do

348

Strany počet

14

URL

https://link.springer.com/chapter/10.1007/978-3-031-71397-2_21

BibTex

@inproceedings{BUT193497,
  author="Adam {Ligocki} and Petr {Gábrlík} and Luděk {Žalud} and Karel {Michenka}",
  title="Utilizing a CNN for Automatic Detection of Military Reconnaissance and Surveillance Objects in Aerial Images: Concept and Challenges",
  booktitle="Lecture Notes in Computer Science",
  year="2024",
  journal="Lecture Notes in Computer Science",
  volume="14615",
  pages="335--348",
  publisher="Springer",
  address="Cham",
  doi="10.1007/978-3-031-71397-2\{_}21",
  isbn="978-3-031-71396-5",
  issn="0302-9743",
  url="https://link.springer.com/chapter/10.1007/978-3-031-71397-2_21"
}