Multi-distortion suppression for neutron radiographic images based on generative adversarial network

Meng, Cheng-Bo; Zhu, Wang-Wei; Zhang, Zhen; Wang, Zi-Tong; Zhao, Chen-Yi; Qiao, Shuang; Zhang, Tian

doi:10.1007/s41365-024-01445-x

NUCL SCI TECH

2024

DOI: 10.1007/s41365-024-01445-x

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Multi-distortion suppression for neutron radiographic images based on generative adversarial network

Cheng-Bo Meng,

Wang-Wei Zhu,

Zhen Zhang

et al.

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Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification

Zhou,

Yang,

Wang

et al. 2024

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To address the computational complexity and deployment challenges of traditional convolutional neural networks in rice disease identification, this paper proposes an efficient and lightweight model: Ghost Channel Spatial Attention ShuffleNet with Mish-ReLU Adaptive Activation Function (GCA-MiRaNet). Based on ShuffleNet V2, we effectively reduced the model’s parameter count by streamlining convolutional layers, decreasing stacking depth, and optimizing output channels. Additionally, the model incorporates the Ghost Module as a replacement for traditional 1 × 1 convolutions, further reducing computational overhead. Innovatively, we introduce a Channel Spatial Attention Mechanism (CSAM) that significantly enhances feature extraction and generalization aimed at rice disease detection. Through combining the advantages of Mish and ReLU, we designed the Mish-ReLU Adaptive Activation Function (MAAF), enhancing the model’s generalization capacity and convergence speed. Through transfer learning and ElasticNet regularization, the model’s accuracy has notably improved while effectively avoiding overfitting. Sufficient experimental results indicate that GCA-MiRaNet attains a precision of 94.76% on the rice disease dataset, with a 95.38% reduction in model parameters and a compact size of only 0.4 MB. Compared to traditional models such as ResNet50 and EfficientNet V2, GCA-MiRaNet demonstrates significant advantages in overall performance, especially on embedded devices. This model not only enables efficient and accurate real-time disease monitoring but also provides a viable solution for rice field protection drones and Internet of Things management systems, advancing the process of contemporary agricultural smart management.

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Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification

Zhou,

Yang,

Wang

et al. 2024

Agronomy

View full text Add to dashboard Cite

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Multi-distortion suppression for neutron radiographic images based on generative adversarial network

Cited by 1 publication

References 35 publications

Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification

Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification

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