DEBC Detection with Deep Learning
| Autor: | Ian E. Nordeng, Doug Olsen, Ahmad Hasan, Jeremiah Neubert |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Accuracy and precision
Computer science business.industry Deep learning 02 engineering and technology 010501 environmental sciences Python (programming language) 01 natural sciences Convolutional neural network Object detection Electric power transmission Dead end 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business computer High tension 0105 earth and related environmental sciences computer.programming_language |
| Zdroj: | Image Analysis ISBN: 9783319591254 SCIA (1) |
| DOI: | 10.1007/978-3-319-59126-1_21 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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