期刊
MEASUREMENT
卷 138, 期 -, 页码 379-385出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.01.072
关键词
Power line fault detection; Insulator; Bird nest; Convolution neural network; Faster R-CNN
资金
- National Key Research and Development Plan [2018YFB1107402]
- Beijing Science and Technology Plan [D171100006217003]
- National Science Foundation of China [61873016, 61633002]
- Fundamental Research Funds for the Central Universities [YWF-18-BJJ-214]
To realize intelligent fault detection of high voltage line, a deep convolution neural network method based on Faster R-CNN method is proposed to locate the broken insulators and bird nests. With the region proposal network, the Faster R-CNN chooses a random region in the features of the image as the proposal region, and trains them to get the corresponding category and location for a certain component in the image. Since the internal and regional features of the image can be learned, the Faster R-CNN method transforms the problem of target classification into the problem of target detection and recognition. Based on the ResNet-101 network model, the damage of insulators and bird nests in the electric power line can be located effectively. (C) 2019 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据