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Deep Neural Networks Compared for Synthetic Aperture Sonar Target Recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated the effectiveness of large deep neural networks, specifically comparing convolutional neural networks (CNNs) and transformer-based architectures, for automatic target recognition (ATR) in synthetic aperture sonar (SAS) imagery. The study aims to identify the optimal network size, training configurations, and regularization methods to achieve the highest performance in SAS-ATR, addressing challenges like limited labeled training data through techniques such as data augmentation and pretraining. AI

IMPACT This research could lead to more accurate and efficient automatic target recognition systems in sonar imagery, potentially improving applications in defense and underwater exploration.

RANK_REASON The cluster contains a research paper detailing a comparative study of deep neural network architectures for a specific image recognition task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep Neural Networks Compared for Synthetic Aperture Sonar Target Recognition

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21 / 100
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The cluster contains a research paper detailing a comparative study of deep neural network architectures for a specific image recognition task. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, model release
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · C. J. Moore, Alex Hurt, Jordan Malof ·

    Improved Automatic Target Recognition in Synthetic Aperture Sonar Imagery Using Large Deep Neural Networks

    arXiv:2609.01800v1 Announce Type: new Abstract: Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) is a task largely dominated by deep neural networks (DNNs). Most SAS-ATR models use convolutional neural network (CNN) architectures whereas transformer-based arch…

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