Memorias de investigación
Research Publications in journals:
Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings
Year:2019

Research Areas
  • Artificial intelligence,
  • Artificial intelligence (neuronal nets, expert systems, etc),
  • Electric engineers, electronic and automatic (eil),
  • Computer vision,
  • Image processing

Information
Abstract
This paper presents a complete system for automatic recognition and the diagnosis of electrical insulator strings which efficiently combines different deep learning-based components to build a versatile solution to the automation problem of the power line inspection process. To this aim, the proposed system integrates one component responsible for insulator string segmentation and two components in charge of its diagnosis. The insulator string segmentation component consists of a novel fully convolutional network (FCN) architecture, termed Up-Net, which enhances the capabilities of the state-of-the-art U-Net network by introducing new skip connections at certain levels of the architecture. Furthermore, we propose a second variant of the Up-Net network by training it within a generative adversarial network (GAN) framework. The capabilities of the proposed Up-Net variants are incremented by the application of data augmentation and transfer learning techniques, achieving accurate segmentation of the insulator string elements (i.e., discs and caps). Regarding the insulator string diagnosis, we design a convolutional neural network (CNN) which takes as input the mask generated by the insulator string segmentation component and is capable of identifying the absence of a variable number of discs. The second diagnosis component consists of a novel strategy which integrates a Siamese convolutional neural network (SCNN) designed for modeling the similarity between adjacent discs and allowing the detection of several types of disc defects using the same model. The proposed system has been extensively evaluated in several video sequences from real aerial inspections of high-voltage insulators, showing robust insulator recognition and diagnosis capabilities.
International
Si
JCR
Si
Title
Ieee Access
ISBN
2169-3536
Impact factor JCR
4,098
Impact info
Datos JCR del año 2018
Volume
7
10.1109/ACCESS.2019.2931144
Journal number
From page
1
To page
26
Month
JULIO
Ranking
Participants

Research Group, Departaments and Institutes related
  • Creador: Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial