NAS-MFF: NAS-Guided Multiscale Feature Fusion Network With Pareto Optimization for Sonar Images Classification

Chen, Yule; Liang, Hong; Jiao, Shaohua

doi:10.1109/jsen.2024.3375372

IEEE Sensors J.

2024

DOI: 10.1109/jsen.2024.3375372

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NAS-MFF: NAS-Guided Multiscale Feature Fusion Network With Pareto Optimization for Sonar Images Classification

Yule Chen,

Hong Liang,

Shaohua Jiao

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2024

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Cited by 3 publications

References 45 publications

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Hierarchical and progressive learning with key point sensitive loss for sonar image classification

Chen,

Tao,

Zhou

et al. 2024

Multimedia Systems

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Hierarchical and progressive learning with key point sensitive loss for sonar image classification

Chen,

Tao,

Zhou

et al. 2024

Multimedia Systems

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Shuffle-RDSNet: a method for side-scan sonar image classification with residual dual-path shrinkage network

Ge,

Liu,

et al. 2024

J Supercomput

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AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases

Omole,

Rosa,

Saadi

et al. 2024

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Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to substantial production losses. To address this challenge, we propose AgriNAS, a method that integrates a Neural Architecture Search (NAS) framework with an adaptive convolutional architecture specifically designed for plant pathology. AgriNAS employs a novel data augmentation strategy and a Spatial–Time Augmentation (STA) method, and it utilizes a multi-stage convolutional network that dynamically adapts to the complexity of the input data. The proposed AgriNAS leverages powerful GPU resources to handle the intensive computational tasks involved in NAS and model training. The framework incorporates a bi-level optimization strategy and entropy-based regularization to enhance model robustness and prevent overfitting. AgriNAS achieves classification accuracies superior to VGG-19 and a transfer learning method using convolutional neural networks.

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NAS-MFF: NAS-Guided Multiscale Feature Fusion Network With Pareto Optimization for Sonar Images Classification

Cited by 3 publications

References 45 publications

Hierarchical and progressive learning with key point sensitive loss for sonar image classification

Hierarchical and progressive learning with key point sensitive loss for sonar image classification

Shuffle-RDSNet: a method for side-scan sonar image classification with residual dual-path shrinkage network

AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases

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