Insulator identification and self-shattering detection based on mask region with convolutional neural network
Yanli Yang, Ying Wang, Hongyan Jiao · Journal of Electronic Imaging · 2019
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.