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Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

Computers, Materials & Continua 2023年第7期76卷 175-194页

作者： Huaxin Zhou Ziying Fang Yilin Wang Mengjun Tong School of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research Hangzhou311300China School of Humanities and Law Zhejiang A&F UniversityHangzhou311300China Department of Computer Science and Engineering Lehigh UniversityPA18015USA

Plant diseases have become a challenging threat in the agricultural *** learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases ***,deep learning entails extensive data for training,and it may be challenging to collect plant *** though plant datasets can be collected,they may be uneven in *** a result,the problem of classification model overfitting *** study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available ***,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image ***,a gradient penalty mechanism was added to the loss function to improve the training stability of the *** show that the proposed method can best improve the recognition accuracy of the classifier with the doubled *** AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the ***,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original ***,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this *** general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.

关键词： Deep learning ACGAN CA gradient penalty tomato diseases identification

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Machinery Fault Diagnosis for Imbalanced Samples via Coupled Generative Adversarial Networks

Machinery Fault Diagnosis for Imbalanced Samples via Coupled...

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第43届中国控制会议

作者： Yatao Liu Xinliang Zhang Yanqi Wang Yitian Zhou Lijie Jia School of Electrical Engineering and Automation Henan Key Laboratory of Intelligent Detection and Control of Coal Mine EquipmentHenan Polytechnic University Anhui Institute of Information Technology Zhoushan Yangwangnaxin Technology Co.Ltd

When applying deep learning networks to the machinery fault diagnosis, it is crucial to train the networks under the sufficient fault samples. To achieve such condition under the imbalance samples, it is essential for the generative networks to learn a complete distribution for the imbalanced samples. Existing data augmentation methods often employ generative models directly to reconstruct samples. Under the limited number of samples, it is a challenge to achieve discriminative features for the fault due to the sample underrepresentation. To address this issue, this paper introduces an innovative approach using coupled generative adversarial networks(CoGAN). The CoGAN framework integrates several systematic enhancements to enhance the robustness of the generative adversarial networks. Firstly, to tackle the imbalance between fault and normal sample distributions,parallel coupled networks are employed to jointly learn their distributions. These networks share weights in the extraction layers of the generator, serving as a representative feature distribution generator for the raw samples. Secondly, a one-sided label smoothing strategy is adopted to mitigate model overfitting in scenarios with limited samples. Finally, historical averaging and gradient penalty are incorporated into the cost function to mitigate adversarial vulnerabilities, ensuring a more stable training performance. Experimental results on the roll bearing dataset from Case Western Reserve University demonstrate that the proposed approach consistently achieves higher accuracy compared to state-of-the-art models across different training sample sizes. This effectively enhances fault diagnosis performance and significantly reduces the dependency on extensive training data.

关键词： deep learning,imbalanced samples fault diagnosis coupled generative model adversarial networks gradient penalty

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