GPU Cuda JSEG Segmentation Algorithm associated with Deep Learning Classifier for Electrical Network Images Identification

Fambrini, Francisco; Iano, Yuzo; Caetano, Dorgival; Rodriguez, Abel A.D.; Moya, Clodoaldo Jose; Carrara, Eduardo; Arthur, Rangel; Cabello, Frank C.; Zubem, João Von; Cura, Luis Mariano Del Val; Filho, João Batista Destro; Campos, José Roberto; Saito, José Hiroki

doi:10.1016/j.procs.2018.07.290

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…The research identified various parameters and methods that can be used to assess the health of power transformers such as internal fault detectors, Recovery Voltage technique, Furan Current Analysis, Expert system based software, Frequency Response Analysis, online PD detection, Dissolved Gas Analysis, and Power quality systems. The authors highlighted the merits and demerits of each method and applied six case studies of IRT to depict different locations of hotspots in power transformers of different ratings and recommended IRT for effective, safe, and efficient CM Sangeetha et al [32] The research obtained single-and dual-dimensional relationships between the distance of image capture, emissivity, and hotspot temperature having derived a relationship between the aforementioned three parameters Fambrini et al [33] The research presented an auto-IRT-based system for fault real-time monitoring of power distribution networks using deep learning image processing-based neural networks. The legacy JSEG IR image segmentation was used and the result proved the method would supersede the manual monitoring method Sahu et al [34] The work presented an IRT methodology for monitoring aging acceleration in transformer insulation, by calculating its per unit life.…”

Section: Mariprasath and Kirubakaran [31]mentioning

confidence: 99%

Techniques of infrared thermography for condition monitoring of electrical power equipment

Ukiwe,

Adeshina,

Tsado

2023

Journal of Electrical Systems and Inf Technol

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The application of computer vision continues to widen with advancement in technology. Imaging systems which provide necessary inputs to the computer-vision-based models can come in various ways. Such as X-ray images, Computed Tomography (CT) scan images, and Infrared (IR) images. This paper is a review of different application areas of infrared thermography (IRT) for monitoring the status of electrical power equipment. It summarizes in tabular form recent research and relevant works within the field of condition monitoring of power assets. A general review of the application of IRT in power devices was undertaken before a specific review of selected works based on IRT for important electrical power equipment with a tabular review of possible causes of hotspots using photovoltaic installation as a reference. Results of previous works were presented with highlights on performance metrics used and accuracies achieved. Emphasis where made on the future potential of IRT and some associated techniques. The work saw that heat production within systems during operation is an important characteristic that enables IRT to become applicable for monitoring diverse physical systems, most importantly power systems. The high cost of high-definition, and long-range IR cameras limits the wide adoption of the technology for its potential applications for monitoring power installations. The work recommends future research in the development of affordable IR imaging systems with advanced features for condition monitoring of physical systems such as power installations.

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Section: Mariprasath and Kirubakaran [31]mentioning

confidence: 99%

Techniques of infrared thermography for condition monitoring of electrical power equipment

Ukiwe,

Adeshina,

Tsado

2023

Journal of Electrical Systems and Inf Technol

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“…With the rapid development of deep learning and the disadvantages of versatility and stability of traditional image processing methods [15][16][17]. Deep learning methods are gradually being used to detect power line [2,18,19]. Hui al et.…”

Section: Research Vision-based Uav Distribution Line Inspection Using...mentioning

confidence: 99%

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Chen

Zhuang

Wang

et al. 2022

Preprint

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Regular inspection of distribution line is an important link to maintain the normal operation of distribution network. Using unmanned aerial vehicle (UAV) instead of manpower can save the cost of inspection. With the universal application of vision sensor in UAV and the rapid development of deep learning, Convolutional Neural Networks (CNN) is applied to the detection of power line in UAV visible images. In view of the lack of application environment inspection methods for distribution line, a vision-based UAV distribution line inspection method using deep learning and a dataset for the deep learning method of distribution line inspection task are proposed in this paper. The method proposed predicts distribution line area through the encoder-decoder structure network firstly. Image processing operation and sampling clustering are used to remove the interference. Finally, the UAV tracking direction of distribution power line is calculated according to the detected distribution line. The method can reach an inspection speed of nearly 77ms per frame, the range of heading deviation error can reach (-1.52°, 1.36°), the tracking rate nearly 100%. Through the test of network and inspection method on dataset, the results show that the method proposed in this paper can be effectively, quickly and accurately applied to UAV distribution line inspection.

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“…Imagen térmica de un equipo de alta tensión [15] A continuación, se exponen las investigaciones más relevantes del uso de técnicas tradicionales de inteligencia artificial en la clasificación de imágenes térmicas. Un claro ejemplo es la propuesta de un sistema de reconocimiento automático para la clasificación de imágenes termográficas de una red de distribución de energía eléctrica [16], en donde se implementó una CNN y el algoritmo JSEG o segmentación J, el cual consiste en una reducción del número de colores y la fusión de los mismos basado en la similitud de las regiones de las imágenes [17]. Al igual que una investigación realizada en el Departamento de Tecnología de Chongqing, China [18], donde se aborda la visión por computadora mediante el uso de imágenes térmicas infrarrojas capturadas sin perturbar el funcionamiento de las subestaciones eléctricas.…”

Section: Figura 3 Validación Cruzada Con K-foldsunclassified

Transfer learning en la clasificación binaria de imágenes térmicas

2021

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La clasificación de imágenes térmicas es un aspecto clave en el sector industrial, debido a que suele ser el punto de partida en la detección de fallos en equipos eléctricos. En algunos casos, esta tarea se automatiza mediante el uso de técnicas tradicionales de inteligencia artificial, mientras que en otros, es realizada de manera manual, lo cual puede traer consigo altas tasas de error humano. Este artículo presenta un análisis comparativo entre once arquitecturas de transfer learning (AlexNet, VGG16, VGG19, ResNet, DenseNet, MobileNet v2, GoogLeNet, ResNeXt, Wide ResNet, MNASNet y ShuffleNet) mediante el uso de fine-tuning, con la finalidad de realizar una clasificación binaria de imágenes térmicas en una red de distribución eléctrica. Para ello, se dispone de una base de datos con 815 imágenes, divididas mediante la técnica tipo hold-out 60-20-20 y validación cruzada con 5-folds, para finalmente analizar su rendimiento mediante el test de Friedman. Luego de los experimentos, se obtuvieron resultados satisfactorios con exactitudes superiores a 85 % en diez de las arquitecturas previamente entrenadas. Sin embargo, la arquitectura que no se entrenó previamente presentó una exactitud baja; concluyéndose que la aplicación de transfer learning mediante el uso de arquitecturas previamente entrenadas es un mecanismo adecuado en la clasificación de este tipo de imágenes, y representa una alternativa confiable frente a técnicas tradicionales de inteligencia artificial.

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GPU Cuda JSEG Segmentation Algorithm associated with Deep Learning Classifier for Electrical Network Images Identification

Cited by 10 publications

References 9 publications

Techniques of infrared thermography for condition monitoring of electrical power equipment

Techniques of infrared thermography for condition monitoring of electrical power equipment

Vision-Based UAV Distribution Line Inspection Using Deep Learning

Transfer learning en la clasificación binaria de imágenes térmicas

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