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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| Published in: | Applied artificial intelligence Vol. 35; no. 6; pp. 476 - 487 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Taylor & Francis
12.05.2021
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| ISSN: | 0883-9514, 1087-6545 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Morocho-Cayamcela, Manuel Eugenio Lim, Wansu |
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| SubjectTerms | 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 |
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| Title | Pattern recognition of soldier uniforms with dilated convolutions and a modified encoder-decoder neural network architecture |
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