Pattern recognition of soldier uniforms with dilated convolutions and a modified encoder-decoder neural network architecture

In this paper, we study a deep learning (DL)-based multimodal technology for military, surveillance, and defense applications based on a pixel-by-pixel classification of soldier's image dataset. We explore the acquisition of images from a remote tactical-robot to a ground station, where the det...

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Veröffentlicht in:Applied artificial intelligence Jg. 35; H. 6; S. 476 - 487
Hauptverfasser: Morocho-Cayamcela, Manuel Eugenio, Lim, Wansu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 12.05.2021
Taylor & Francis Ltd
Taylor & Francis Group
Schlagworte:
Algorithms
Artificial neural networks
Battlefields
Coders
Computer architecture
Datasets
Encoders-Decoders
Ground stations
Image acquisition
Image classification
Image processing
Image segmentation
Machine learning
Military applications
Military technology
Neural networks
Pattern recognition
Pixels
Robots
ISSN:0883-9514, 1087-6545
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Zusammenfassung:In this paper, we study a deep learning (DL)-based multimodal technology for military, surveillance, and defense applications based on a pixel-by-pixel classification of soldier's image dataset. We explore the acquisition of images from a remote tactical-robot to a ground station, where the detection and tracking of soldiers can help the operator to take actions or automate the tactical-robot in battlefield. The soldier detection is achieved by training a convolutional neural network to learn the patterns of the soldier's uniforms. Our CNN learns from the initial dataset and from the actions taken by the operator, as opposed to the old-fashioned and hard-coded image processing algorithms. Our system attains an accuracy of over 81% in distinguishing the specific soldier uniform and the background. These experimental results prove our hypothesis that dilated convolutions can increase the segmentation performance when compared with patch-based, and fully connected networks.
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ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2021.1902124