基于自我注意力和全球-本地融合的基于电源场景缺陷检测的物联网支持的少数镜头图像生成
Yi Chen1, Yunfeng Yan2,3, Xianbo Wang3
1College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种新的生成对抗网络 (MVSA-GAN),用于创建现实的电力设备缺陷图像. 这种方法增强了机器学习模型,以改善缺陷检测,解决电力系统中的数据稀缺性和隐私问题.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在电力系统中检测缺陷对于安全性和可靠性至关重要.
- 现有的方法与大型数据集,数据隐私和不平衡的缺陷类别作斗争.
- 物联网 (IoT) 为电力设备监控中的数据采集提供了新的途径.
研究的目的:
- 为了开发一个先进的少数镜头图像生成技术,用于电源场景缺陷.
- 解决电力系统缺陷检测中的数据隐私和样本失衡问题.
- 提高使用生成合成数据的缺陷检测模型的性能.
主要方法:
- 开发了一个生成对抗网络 (MVSA-GAN),集成多视图融合和自我注意力.
- 物联网设备被用于从电源场景中实时捕获数据.
- 设计了一种自我注意编码器和多视图功能融合模块,以提高图像的真实性和多样性.
主要成果:
- MVSA-GAN模型成功地为动力场景生成了逼真的和多样化的缺陷图像.
- 提出的少数拍摄图像生成方法在克服数据限制方面表现出有效性.
- 取得了优异的FID (67.87) 和LPIPS (0.179) 分数,超过了最先进的方法.
结论:
- MVSA-GAN方法为在电力系统中生成高质量的合成缺陷数据提供了可行的解决方案.
- 这种方法通过减轻数据隐私和不平衡挑战来增强基于机器学习的缺陷检测.
- 这项研究为更强大,更可靠的电力设备监控系统铺平了道路.
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