Chen Yi, Yan Yunfeng, Wang Xianbo, Zheng Yi
College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China.
School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2023 Jul 19;23(14):6531. doi: 10.3390/s23146531.
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.
电力场景中的缺陷检测是一项关键任务，对确保电力系统的安全、可靠和高效起着重要作用。现有技术需要提高其从大量数据中学习的能力，以实现理想的检测效果。电力场景数据涉及隐私和安全问题，并且不同缺陷类别的样本数量存在不平衡，所有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集成给电力设备缺陷检测提供了新方向。同时，提出了一种基于多视图融合和自注意力的生成对抗网络用于少样本图像生成，名为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，使其能够生成逼真且多样的缺陷数据。所设计的自注意力编码器专注于图像不同部分的相关特征，以捕获输入图像的上下文信息并提高图像的真实性和连贯性。提出了一种多视图特征融合模块，通过全局和局部特征的选择性融合来捕获电力场景的复杂结构和纹理，并提高生成图像的真实性和多样性。实验表明，本文提出的少样本图像生成方法可以为电力场景缺陷生成真实且多样的缺陷数据。所提方法的FID和LPIPS分数分别为67.87和0.179，超过了FIGR和DAWSON等当前最优方法。
