research-article

Automatic Visual Recognition of Unexploded Ordnances Using Supervised Deep Learning

Authors:

Georgios Begkas,

Panagiotis Giannakeris,

Konstantinos Ioannidis,

Georgios Kalpakis,

Theodora Tsikrika,

Stefanos Vrochidis,

Ioannis KompatsiarisAuthors Info & Claims

ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval

Pages 286 - 294

https://doi.org/10.1145/3512527.3531383

Published: 27 June 2022 Publication History

Abstract

Unexploded Ordnance (UXO) classification is a challenging task which is currently tackled using electromagnetic induction devices that are expensive and may require physical presence in potentially hazardous environments. The limited availability of open UXO data has, until now, impeded the progress of image-based UXO classification, which may offer a safe alternative at a reduced cost. In addition, the existing sporadic efforts focus mainly on small scale experiments using only a subset of common UXO categories. Our work aims to stimulate research interest in image-based UXO classification, with the curation of a novel dataset that consists of over 10000 annotated images from eight major UXO categories. Through extensive experimentation with supervised deep learning we uncover key insights into the challenging aspects of this task. Finally, we set the baseline on our novel benchmark by training state-of-the-art Convolutional Neural Networks and a Vision Transformer that are able to discriminate between highly overlapping UXO categories with 84.33% accuracy.

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Cited By

Craioveanu MStamatescu G(2024)Detection and Identification of Unexploded Ordnance Using a Two-Step Deep Learning Methodology2024 32nd Mediterranean Conference on Control and Automation (MED)10.1109/MED61351.2024.10566207(257-262)Online publication date: 11-Jun-2024
https://doi.org/10.1109/MED61351.2024.10566207

Index Terms

Automatic Visual Recognition of Unexploded Ordnances Using Supervised Deep Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
        Visual content-based indexing and retrieval

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cover image ACM Conferences

ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval

June 2022

714 pages

ISBN:9781450392389

DOI:10.1145/3512527

General Chairs:
Vincent Oria
New Jersey Institute of Technology, USA
,
Maria Luisa Sapino
Università degli Studi di Torino, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Brigitte Kerhervé
Université du Québec à Montréal, Canada
,
Program Chairs:
Wen-Huang Cheng
National Yang Ming Chao Tung University, Taiwan
,
Ichiro Ide
Nagoya University, Japan
,
Vivek Singh
Rutgers University, USA

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Published: 27 June 2022

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ICMR '22

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ICMR '22: International Conference on Multimedia Retrieval

June 27 - 30, 2022

NJ, Newark, USA

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Craioveanu MStamatescu G(2024)Detection and Identification of Unexploded Ordnance Using a Two-Step Deep Learning Methodology2024 32nd Mediterranean Conference on Control and Automation (MED)10.1109/MED61351.2024.10566207(257-262)Online publication date: 11-Jun-2024
https://doi.org/10.1109/MED61351.2024.10566207

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