SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

Zhao, Yun; Chen, Jiagui; Xu, Xing; Lei, Jingsheng; Zhou, Wujie

doi:10.1002/cpe.6161

Cited by 33 publications

(15 citation statements)

References 52 publications

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“…This model removes the nonlocal attention mechanism in AM-GAN, and other structures are the same as the AM-GAN. In addition, current mainstream, AM-FCN [ 43 ] embedded based on attention, Attention U-Net [ 44 ] fused on attention in both encoder and decoder, DANet [ 45 ] embedded based on dual attention, and SEVNet [ 46 ] fused on SE module are selected as comparison models. The four index values of accuracy, recall, precision, and F1-score are used for comparative evaluation.…”

Section: The Experiments and Results Analysismentioning

confidence: 99%

A Generative Adversarial Network Fused with Dual‐Attention Mechanism and Its Application in Multitarget Image Fine Segmentation

Yin

Zhou

et al. 2021

Computational Intelligence and Neuroscience

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Aiming at the problem of insignificant target morphological features, inaccurate detection and unclear boundary of small-target regions, and multitarget boundary overlap in multitarget complex image segmentation, combining the image segmentation mechanism of generative adversarial network with the feature enhancement method of nonlocal attention, a generative adversarial network fused with attention mechanism (AM-GAN) is proposed. The generative network in the model is composed of residual network and nonlocal attention module, which use the feature extraction and multiscale fusion mechanism of residual network, as well as feature enhancement and global information fusion ability of nonlocal spatial-channel dual attention to enhance the target features in the detection area and improve the continuity and clarity of the segmentation boundary. The adversarial network is composed of fully convolutional networks, which penalizes the loss of information in small-target regions by judging the authenticity of prediction and label segmentation and improves the detection ability of the generative adversarial model for small targets and the accuracy of multitarget segmentation. AM-GAN can use the GAN’s inherent mechanism that reconstruct and repair high-resolution image, as well as the ability of nonlocal attention global receptive field to strengthen detail features, automatically learn to focus on target structures of different shapes and sizes, highlight salient features useful for specific tasks, reduce the loss of image detail features, improve the accuracy of small-target detection, and optimize the segmentation boundary of multitargets. Taking medical MRI abdominal image segmentation as a verification experiment, multitargets such as liver, left/right kidney, and spleen are selected for segmentation and abnormal tissue detection. In the case of small and unbalanced sample datasets, the class pixels’ accuracy reaches 87.37%, the intersection over union is 92.42%, and the average Dice coefficient is 93%. Compared with other methods in the experiment, the segmentation precision and accuracy are greatly improved. It shows that the proposed method has good applicability for solving typical multitarget image segmentation problems such as small-target feature detection, boundary overlap, and offset deformation.

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Section: The Experiments and Results Analysismentioning

confidence: 99%

A Generative Adversarial Network Fused with Dual‐Attention Mechanism and Its Application in Multitarget Image Fine Segmentation

Yin

Zhou

et al. 2021

Computational Intelligence and Neuroscience

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“…In this section, we compared our method with nine state-of-the-art methods, Among these methods, traditional segmentation methods using CNNs were included, such as SegNet [12], Bayesian SegNet [13], AttentionUNet [16] , CE-Net [17], MM-LinkNet [14], DFN [15],and SEVnet [18]. Also include some segmentation methods using DPM, such as EnsemDiff [9] and MedsegDiff [10].…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…In brain tumor segmentation, some common approaches involve using variants of U-Net to predict the segmentation masks for the input, such as [16,17,18]. Their common feature is the use of an encoder-decoder structure to extract features and then restore them to the original resolution.…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

Uncertainty-generation-based diffusion probability model for brain tumor segmentation

qin,

Zhang,

Yuan

et al. 2024

Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024)

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“…This section will provide an overview of the CBAM attention mechanism. Woo proposed CBAM, which would be comprised of two modules: the channel attention module and the space attention module ( Bao et al, 2021 ; Gao et al, 2021 ; Zhao Y. et al, 2021 ). The CBAM module is shown in Figure 9 .…”

Section: Methodsmentioning

confidence: 99%

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

Jin

et al. 2022

Front. Plant Sci.

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The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.

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SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

Cited by 33 publications

References 52 publications

A Generative Adversarial Network Fused with Dual‐Attention Mechanism and Its Application in Multitarget Image Fine Segmentation

A Generative Adversarial Network Fused with Dual‐Attention Mechanism and Its Application in Multitarget Image Fine Segmentation

Uncertainty-generation-based diffusion probability model for brain tumor segmentation

Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

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