IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global-Local Fusion.
IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global-Local Fusion.
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DOI：
10.3390/s23146531
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发表时间：
2023-07-19
期刊：
影响因子：
--
通讯作者：
Zheng Y
中科院分区：
文献类型：
--
作者：
Chen Y;Yan Y;Wang X;Zheng Y
电力系统故障检测是电力系统安全、可靠、高效运行的关键环节。现有技术需要增强其从大量数据中学习的能力，以达到理想的检测效果。电源场景数据涉及隐私和安全问题，并且不同缺陷类别之间的样本数量存在不平衡，这些都会影响缺陷检测模型的性能。随着物联网（IoT）的出现，物联网与机器学习的融合为电力设备的缺陷检测提供了新的方向。同时，提出了一种基于多视角融合和自注意的生成式对抗网络，称为MVSA-GAN。物联网设备从电力场景中捕获实时数据，然后用于训练MVSA-GAN模型，使其能够生成真实和多样化的缺陷数据。所设计的自注意编码器关注图像不同部分的相关特征，捕捉输入图像的上下文信息，提高图像的真实性和连贯性。提出了一种多视角特征融合模块，通过全局和局部特征的选择性融合，捕捉电力场景的复杂结构和纹理，提高生成图像的真实性和多样性。实验表明，本文提出的少镜头图像生成方法能够生成真实的、多样的电力场景缺陷数据。该方法的FID和LPIPS得分分别为67.87和0.179，超过了SOTA方法，如FIGR和DAWSON。
Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON.
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