DEBC Detection with Deep Learning

Ian E. Nordeng, Ahmad Hasan, Doug Olsen, Jeremiah Neubert

Abstract: This work presents a novel system utilizing state of the art deep convolutional neural networks to detect dead end body component’s (DEBC’s) to reduce costs for inspections and maintenance of high tension power lines. A series of data augmenting techniques were implemented to develop 2,437 training images which utilized 146 images from a sensor trade study, and a test flight using UAS for inspections. Training was completed using the Python implementation of Faster R-CNN’s object detection network with the VGG16 model. After testing the network on 111 aerial inspection photos captured with an UAS, the resulting convolutional neural network (CNN) was capable of an accuracy of 83.7% and precision of 91.8%. The addition of 270 training images and inclusion of insulators increased detection accuracy and precision to 97.8% and 99.1% respectively.

External IDs:dblp:conf/scia/NordengHON17