Artificial Intelligence in Explosive Ordnance Disposal Tasks
Software Basics of EOD Support Information System, Part III
Copyright (c) 2024 Ádám Balázs
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abstract
The main goal of the four-part article series entitled Artificial Intelligence in EOD Tasks is to present the EOD Support Information System Based on Artificial Intelligence. In Part 3 of the series of articles, the essence of the software and mathematical background of image processing, on the basis of which the artificial intelligence classifies a given image into one of the predefined groups. The operation of the image processing software is the most important element of the EOD supporting program.
Keywords:
References
Analog Image Processing vs. Digital Image Processing [é. n.]. Online: https://www.javatpoint.com/analog-image-processing-vs-digital-image-processing
Brownlee, Jason (2020): How Do Convolutional Layers Work in Deep Learning Neural Networks? Online: https://machinelearningmastery.com/convolutional-layers-for-deep-learning-neural-networks/
Dertat, Arden (2017): Applied Deep Learning–Part 4: Convolutional Neural Networks. Online: https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2
Ember István (2020a): A lőszermentesítés szerepe az építőiparban. Építőanyag, 72(2), 59–63. Online: https://doi.org/10.14382/epitoanyag-jsbcm.2020.9
Ember István (2020b): The Role and the Risks of Explosive Ordnance Decontamination in Hungary. Science & Military (Veda a Vojenstvo), 16(1), 32–42. Online: https://doi.org/10.52651/sam.a.2021.1.32-42
Image Processing Definition, Examples and Application [é. n.]. Online: https://www.zr-tech.co.uk/Image-Processing-/
Korstanje, Joos (2020): What is the Difference Between Object Detection and Image Segmentation? Online: https://towardsdatascience.com/what-is-the-difference-between-object-detection-and-image-segmentation-ee746a935cc1
Kovács Zoltán (2012): Az improvizált robbanóeszközök főbb típusai. Műszaki Katonai Közlöny, 22(2), 37–52. Online: https://mkk.uni-nke.hu/document/mkk-uni-nke-hu/2012_2_03%20IED-k%20f%C5%91bb%20t%C3%ADpusai%20-%20Kov%C3%A1cs%20Z.pdf
Max Pooling [é. n.]. Online: https://paperswithcode.com/method/max-pooling
Németh András – Virágh Krisztián (2022): Mesterséges intelligencia és haderő – Polgári alkalmazási lehetőségek V. rész. Haditechnika, 56(5), 2–7. Online: https://doi.org/10.23713/HT.56.5.01
Németh András – Virágh Krisztián (2023): Mesterséges intelligencia és haderő – Katonai alkalmazási lehetőségek VII. rész. Haditechnika, 57(1), 2–6. Online: https://doi.org/10.23713/HT.57.1.01
Objektum felismerő (képfelismerő) AI megoldásunk (2019). Online: https://www.regens.com/hu/-/objektum-felismeres-kepfelismeres-mesterseges-intelligenciaval-bemutato-video
Sharma, Pulkit (2019): Image Classification vs. Object Detection vs. Image Segmentation. Online: https://medium.com/analytics-vidhya/image-classification-vs-object-detection-vs-image-segmentation-f36db85fe81
Teachable Machine [é. n.]. Online: https://teachablemachine.withgoogle.com/
Tomolya János – Padányi József (2012): A terrorizmus jelentette kihívások. Hadtudomány, 22(3–4), 34–67. Online: https://www.mhtt.eu/hadtudomany/2012/3_4/HT_2012_3-4_Tomolya_Padanyi.pdf
Tóth László (2019): Mesterséges neuronhálók és alkalmazásaik. Online: https://www.inf.u-szeged.hu/~tothl/ann/Neuronhalok-egyben.pdf
What Is Deep Learning? 3 Things You Need to Know [é. n.]. Online: https://www.mathworks.com/discovery/deep-learning.html