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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.<\/jats:p>","DOI":"10.3390\/s23146531","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T21:22:58Z","timestamp":1689801778000},"page":"6531","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global\u2013Local Fusion"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5919-6413","authenticated-orcid":false,"given":"Yi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0939-5526","authenticated-orcid":false,"given":"Yunfeng","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"Hainan Institute of Zhejiang University, Sanya 572025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0463-2983","authenticated-orcid":false,"given":"Xianbo","family":"Wang","sequence":"additional","affiliation":[{"name":"Hainan Institute of Zhejiang University, Sanya 572025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7021-5320","authenticated-orcid":false,"given":"Yi","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.comnet.2018.11.025","article-title":"Current research on Internet of Things (IoT) security: A survey","volume":"148","author":"Hassan","year":"2019","journal-title":"Comput. 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