Insulator identification and self-shattering detection based on mask region with convolutional neural network.
- Resource Type
- Article
- Authors
- Yang, Yanli; Wang, Ying; Jiao, Hongyan
- Source
- Journal of Electronic Imaging. Sep/Oct2019, Vol. 28 Issue 5, p1-10. 10p.
- Subject
- *ELECTRIC lines
*ELECTRIC power distribution grids
- Language
- ISSN
- 1017-9909
As a component of a power transmission line, the state of an insulator impacts the reliability and safety of the power grid. Self-shattering is an important factor that may cause insulator anomalies. We present a method for detecting insulator self-shattering using mask regions with a convolutional neural network, namely a mask region convolutional neural network. The method can locate fault insulators while finding the fault image with insulator self-shattering. It can also find the insulators and distinguish between normal and self-shattering even if there are multiple insulators in an image. The insulator self-shattering detection program is written in TensorFlow and the Keras deep learning framework. Experiments are conducted on 810 real-world images. The testing results show that the mean average precision can be up to 1 for single-target images and 0.948 for multitarget images. [ABSTRACT FROM AUTHOR]