Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings
Año:2019
Áreas de investigación
  • Inteligencia artificial,
  • Inteligencia artificial (redes neuronales, lógica borrosa, sistemas expertos, etc),
  • Ingeniería eléctrica, electrónica y automática,
  • Visión por computador,
  • Procesamiento de imágenes
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
Ieee Access
ISSN
2169-3536
Factor de impacto JCR
4,098
Información de impacto
Datos JCR del año 2018
Volumen
7
DOI
10.1109/ACCESS.2019.2931144
Número de revista
Desde la página
1
Hasta la página
26
Mes
JULIO
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Carlos Sampedro Perez (UPM)
  • Autor: Francisco Javier Rodriguez Vazquez (UPM)
  • Autor: Alejandro Rodriguez Ramos (UPM)
  • Autor: Adrián Carrió Fernández (UPM)
  • Autor: Pascual Campoy Cervera (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
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