• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议> >Toward Automated Utility Pole Condition Monitoring: A Deep Learning Approach
【24h】

Toward Automated Utility Pole Condition Monitoring: A Deep Learning Approach

机译：走向自动化电线杆状态监测：一种深度学习方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As infrastructure ages, grid operators across the world are becoming more cognizant of the need for monitoring their transmission infrastructure. The geographic extent of the transmission system, however, makes this a difficult and expensive task with thousands of components requiring visual inspection to identify faults which can lead to potentially catastrophic failures. This paper describes the use of Deep Neural Networks to automatically detect areas of concrete damage on utility poles in a European utility from photographs. This is beneficial to reduce time spent in the field as well as variability in between human assessment. The results show that even with a small dataset for training, the network is able to identify new damage with a high level of precision.
机译：作为基础设施年龄，世界各地的网格运营商正在变得越来越认识到监测其传输基础设施的需求。然而，传输系统的地理范围使得这是一种困难而昂贵的任务，具有数千个需要视觉检查以识别可能导致可能灾难性失败的故障的组件。本文介绍了使用深神经网络在欧洲公用事业中自动检测欧洲公用事业杆的混凝土损坏区域。这有利于减少现场的时间以及人类评估之间的可变性。结果表明，即使具有用于训练的小型数据集，网络也能够以高精度识别新的损坏。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号